Tootfinder

CounterMeasure – The Profitable Estimator Podcast
Great Australian Pods Podcast Directory: https://www.greataustralianpods.com/countermeasure-the-profitable-estimator-podcast/

A generalized Hessian-based error estimator for an IPDG formulation of the biharmonic problem in two dimensions
Th\'eophile Chaumont-Frelet, Joscha Gedicke, Lorenzo Mascotto
https://arxiv.org/abs/2507.05776

A generalized Hessian-based error estimator for an IPDG formulation of the biharmonic problem in two dimensions
We consider a two dimensional biharmonic problem and its discretization by means of a symmetric interior penalty discontinuous Galerkin method. Based on the ``div-div'' complex, a novel split of an error measure based on a generalized Hessian into two terms measuring the conformity and nonconformity of the scheme is proven. This splitting is the departing point for the design of a new reliable and efficient error estimator, which does not involve any DG stabilization. Such an error estimator ca…

Noise Analysis and Hierarchical Adaptive Body State Estimator For Biped Robot Walking With ESVC Foot
Boyang Chen, Xizhe Zang, Chao Song, Yue Zhang, Xuehe Zhang, Jie Zhao
https://arxiv.org/abs/2506.08578

Noise Analysis and Hierarchical Adaptive Body State Estimator For Biped Robot Walking With ESVC Foot
The ESVC(Ellipse-based Segmental Varying Curvature) foot, a robot foot design inspired by the rollover shape of the human foot, significantly enhances the energy efficiency of the robot walking gait. However, due to the tilt of the supporting leg, the error of the contact model are amplified, making robot state estimation more challenging. Therefore, this paper focuses on the noise analysis and state estimation for robot walking with the ESVC foot. First, through physical robot experiments, we …

Stochastic Trace Optimization of Parameter Dependent Matrices Based on Statistical Learning Theory
Arvind K. Saibaba, Ilse C. F. Ipsen
https://arxiv.org/abs/2508.05764 https://

Stochastic Trace Optimization of Parameter Dependent Matrices Based on Statistical Learning Theory
We consider matrices $\boldsymbol{A}(\boldsymbolθ)\in\mathbb{R}^{m\times m}$ that depend, possibly nonlinearly, on a parameter $\boldsymbolθ$ from a compact parameter space $Θ$. We present a Monte Carlo estimator for minimizing $\text{trace}(\boldsymbol{A}(\boldsymbolθ))$ over all $\boldsymbolθ\inΘ$, and determine the sampling amount so that the backward error of the estimator is bounded with high probability. We derive two types of bounds, based on epsilon nets and on generic chaining. B…

Correction of estimator bias in linear regression with categorical covariates with classification error
Alexandre Garcia Dias, Mariana Rodrigues Motta, Alexandre Hild Aono
https://arxiv.org/abs/2507.07245

Correction of estimator bias in linear regression with categorical covariates with classification error
The objective of this work is to propose an asymptotic correction method for the estimators of parameters from regression models with covariates subject to classification errors. A correction was developed based on the least squares estimators from regression with erroneous covariates, the marginal probability of the true covariates, and the conditional probability of the erroneous covariates given the true covariates. In this way, we can correct these estimators without the need to correct the…

On the Estimation of Gaussian Moment Tensors
Omar Al-Ghattas, Jiaheng Chen, Daniel Sanz-Alonso
https://arxiv.org/abs/2507.06166 https://

On the Estimation of Gaussian Moment Tensors
This paper studies two estimators for Gaussian moment tensors: the standard sample moment estimator and a plug-in estimator based on Isserlis's theorem. We establish dimension-free, non-asymptotic error bounds that demonstrate and quantify the advantage of Isserlis's estimator for tensors of even order $p>2$. Our bounds hold in operator and entrywise maximum norms, and apply to symmetric and asymmetric tensors.

Neural Actor-Critic Methods for Hamilton-Jacobi-Bellman PDEs: Asymptotic Analysis and Numerical Studies
Samuel N. Cohen, Jackson Hebner, Deqing Jiang, Justin Sirignano
https://arxiv.org/abs/2507.06428

Neural Actor-Critic Methods for Hamilton-Jacobi-Bellman PDEs: Asymptotic Analysis and Numerical Studies
We mathematically analyze and numerically study an actor-critic machine learning algorithm for solving high-dimensional Hamilton-Jacobi-Bellman (HJB) partial differential equations from stochastic control theory. The architecture of the critic (the estimator for the value function) is structured so that the boundary condition is always perfectly satisfied (rather than being included in the training loss) and utilizes a biased gradient which reduces computational cost. The actor (the estimator f…

Estimating Social Network Models with Link Misclassification
Arthur Lewbel, Xi Qu, Xun Tang
https://arxiv.org/abs/2509.07343 https://arxiv.org/pdf/2509.073…

Estimating Social Network Models with Link Misclassification
We propose an adjusted 2SLS estimator for social network models when reported binary network links are misclassified (some zeros reported as ones and vice versa) due, e.g., to survey respondents' recall errors, or lapses in data input. We show misclassification adds new sources of correlation between the regressors and errors, which makes all covariates endogenous and invalidates conventional estimators. We resolve these issues by constructing a novel estimator of misclassification rates and us…

Neural Estimation of Information Leakage for Secure Communication System Design
Darius S. Heerklotz, Ingo Schroeder, Pin-Hsun Lin, Christian Deppe, Eduard A. Jorswieck
https://arxiv.org/abs/2508.05176 …

Neural Estimation of Information Leakage for Secure Communication System Design
Underestimating the leakage can compromise secrecy, while overestimating it may lead to inefficient system design. Therefore, a reliable leakage estimator is essential. Neural network-based estimators provide a data-driven way to estimate mutual information without requiring full knowledge of the channel or source distributions. In this work, we aim to scale the blocklength of a wiretap code such that the estimator can still feasibly operate. We propose an improved mutual information estimator …

In-Context Learning as an Effective Estimator of Functional Correctness of LLM-Generated Code
Susmita Das, Madhusudan Ghosh, Priyanka Swami, Debasis Ganguly, Gul Calikli
https://arxiv.org/abs/2507.05200

In-Context Learning as an Effective Estimator of Functional Correctness of LLM-Generated Code
When applying LLM-based code generation to software development projects that follow a feature-driven or rapid application development approach, it becomes necessary to estimate the functional correctness of the generated code in the absence of test cases. Just as a user selects a relevant document from a ranked list of retrieved ones, a software generation workflow requires a developer to choose (and potentially refine) a generated solution from a ranked list of alternative solutions, ordered …

Besting Good--Turing: Optimality of Non-Parametric Maximum Likelihood for Distribution Estimation
Yanjun Han, Jonathan Niles-Weed, Yandi Shen, Yihong Wu
https://arxiv.org/abs/2509.07355

Besting Good--Turing: Optimality of Non-Parametric Maximum Likelihood for Distribution Estimation
When faced with a small sample from a large universe of possible outcomes, scientists often turn to the venerable Good--Turing estimator. Despite its pedigree, however, this estimator comes with considerable drawbacks, such as the need to hand-tune smoothing parameters and the lack of a precise optimality guarantee. We introduce a parameter-free estimator that bests Good--Turing in both theory and practice. Our method marries two classic ideas, namely Robbins's empirical Bayes and Kiefer--Wolfo…

Doubly robust integration of nonprobability and probability survey data
Shaun R Seaman, Tommy Nyberg, Anne M Presanis
https://arxiv.org/abs/2508.05859 https://

Doubly robust integration of nonprobability and probability survey data
Doubly robust estimators combine an inverse probability weighting estimator and a mass imputation estimator. Several doubly robust estimators for estimating the population mean (or prevalence) of an outcome have been proposed for integrating outcome and covariate data from a nonprobability survey with covariate data from an auxiliary probability survey. However, the question of how to combine a doubly robust estimate with a corresponding estimate based on outcome data from the auxiliary probabi…

Replaced article(s) found for cs.DB. https://arxiv.org/list/cs.DB/new
[1/1]:
- DistJoin: A Decoupled Join Cardinality Estimator based on Adaptive Neural Predicate Modulation
Kaixin Zhang, Hongzhi Wang, Ziqi Li, Yabin Lu, Yingze Li, Yu Yan, Yiming Guan

Joint Target Acquisition and Refined Position Estimation in OFDM-based ISAC Networks
Lorenzo Pucci, Andrea Giorgetti
https://arxiv.org/abs/2507.07081 https…

Joint Target Acquisition and Refined Position Estimation in OFDM-based ISAC Networks
This paper addresses joint target acquisition and position estimation in an OFDM-based integrated sensing and communication (ISAC) network with base station (BS) cooperation via a fusion center. A two-stage framework is proposed: in the first stage, each BS computes range-angle maps to detect targets and estimate coarse positions, exploiting spatial diversity. In the second stage, refined localization is performed using a cooperative maximum likelihood (ML) estimator over predefined regions of …

Gradient and Hessian-Based Temperature Estimator in Lattice Gauge Theories: A Diagnostic Tool for Stability and Consistency in Numerical Simulations
Navdeep Singh Dhindsa, Anosh Joseph, Vamika Longia
https://arxiv.org/abs/2508.05595

Gradient and Hessian-Based Temperature Estimator in Lattice Gauge Theories: A Diagnostic Tool for Stability and Consistency in Numerical Simulations
We present a field configuration-based temperature estimator in lattice gauge theories. It is constructed from the gradient and Hessian of the Euclidean action of the theory. Adapted from geometric formulations of entropy in classical statistical mechanics, this estimator provides a gauge-invariant, non-kinetic diagnostic of thermodynamic consistency in Monte Carlo simulations of lattice gauge theories. We validate the method in compact U(1) lattice gauge theories across one, two, and four dime…

Deuteron identification via time of flight with LHCb
LHCb collaboration, et al.
https://arxiv.org/abs/2508.06305 https://arxiv.org/pdf/2508.06305

Deuteron identification via time of flight with LHCb
It is shown that the timing capabilities of the LHCb detector operated during the LHC Run 2 can be used to identify light ion particles with momenta of a few GeV/$c$. This is achieved by estimating the particle time of flight through a newly developed technique. A dedicated reconstruction procedure and a neural-network-based estimator of the particle speed have been developed to enable deuteron identification by suppressing the abundant background from lighter particles. The performance of the …

From Editor to Dense Geometry Estimator
JiYuan Wang, Chunyu Lin, Lei Sun, Rongying Liu, Lang Nie, Mingxing Li, Kang Liao, Xiangxiang Chu, Yao Zhao
https://arxiv.org/abs/2509.04338

From Editor to Dense Geometry Estimator
Leveraging visual priors from pre-trained text-to-image (T2I) generative models has shown success in dense prediction. However, dense prediction is inherently an image-to-image task, suggesting that image editing models, rather than T2I generative models, may be a more suitable foundation for fine-tuning. Motivated by this, we conduct a systematic analysis of the fine-tuning behaviors of both editors and generators for dense geometry estimation. Our findings show that editing models possess i…

Minimax optimal transfer learning for high-dimensional additive regression
Seung Hyun Moon
https://arxiv.org/abs/2509.06308 https://arxiv.org/pdf/2509.0630…

Minimax optimal transfer learning for high-dimensional additive regression
This paper studies high-dimensional additive regression under the transfer learning framework, where one observes samples from a target population together with auxiliary samples from different but potentially related regression models. We first introduce a target-only estimation procedure based on the smooth backfitting estimator with local linear smoothing. In contrast to previous work, we establish general error bounds under sub-Weibull($α$) noise, thereby accommodating heavy-tailed error d…

The median trick does not help for fully nested scrambling
Takashi Goda, Kosuke Suzuki
https://arxiv.org/abs/2507.04297 https://arxiv…

The median trick does not help for fully nested scrambling
In randomized quasi-Monte Carlo methods for numerical integration, average estimators based on digital nets with fully nested and linear scrambling are known to exhibit the same variance. In this note, we show that this equivalence does not extend to the median estimators. Specifically, while the median estimator with linear scrambling can achieve faster convergence for smooth integrands, the median estimator with fully nested scrambling does not exhibit this advantage.

Parametric convergence rate of a non-parametric estimator in multivariate mixtures of power series distributions under conditional independence
Fadoua Balabdaoui, Harald Besdziek, Yong Wang
https://arxiv.org/abs/2509.05452

Parametric convergence rate of a non-parametric estimator in multivariate mixtures of power series distributions under conditional independence
The conditional independence assumption has recently appeared in a growing body of literature on the estimation of multivariate mixtures. We consider here conditionally independent multivariate mixtures of power series distributions with infinite support, to which belong Poisson, Geometric or Negative Binomial mixtures. We show that for all these mixtures, the non-parametric maximum likelihood estimator converges to the truth at the rate $(\log (nd))^{1+d/2} n^{-1/2}$ in the Hellinger distance,…

Galerkin-ARIMA: A Two-Stage Polynomial Regression Framework for Fast Rolling One-Step-Ahead Forecasting
Haojie Liu, Zihan Lin
https://arxiv.org/abs/2507.07469

Galerkin-ARIMA: A Two-Stage Polynomial Regression Framework for Fast Rolling One-Step-Ahead Forecasting
Time-series models like ARIMA remain widely used for forecasting but limited to linear assumptions and high computational cost in large and complex datasets. We propose Galerkin-ARIMA that generalizes the AR component of ARIMA and replace it with a flexible spline-based function estimated by Galerkin projection. This enables the model to capture nonlinear dependencies in lagged values and retain the MA component and Gaussian noise assumption. We derive a closed-form OLS estimator for the Galerk…

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- Ichnos: A Carbon Footprint Estimator for Scientific Workflows
Kathleen West, Magnus Reid, Yehia Elkhatib, Lauritz Thamsen

Consistency of an Intercept-Shifted Synthetic-Control Estimator under Weighted Parallel Trends
Michael Guggisberg
https://arxiv.org/abs/2508.05604 https://…

Consistency of an Intercept-Shifted Synthetic-Control Estimator under Weighted Parallel Trends
The average treatment effect on the treated (ATT) in a staggered-adoption panel is estimated using an intercept-augmented synthetic-control (SCM) estimator. A weighted parallel trends plus an intercept shift, together with mild regularity on the weight vectors (non-degenerate dispersion) and expanding pre-treatment length, are sufficient for consistency allowing for heavy-tailed shocks. These conditions can be more interpretable than the autoregressive or low-rank factor models with light tails…

Deep Inverse Rosenblatt Transport for Structural Reliability Analysis
Aryan Tyagi, Jan N. Fuhg
https://arxiv.org/abs/2509.05061 https://arxiv.org/pdf/2509.…

Deep Inverse Rosenblatt Transport for Structural Reliability Analysis
Accurately estimating the probability of failure in engineering systems under uncertainty is a fundamental challenge, particularly in high-dimensional settings and for rare events. Conventional reliability analysis methods often become computationally intractable or exhibit high estimator variance when applied to problems with hundreds of uncertain parameters or highly concentrated failure regions. In this work, we investigate the use of the recently proposed Deep Inverse Rosenblatt Transport (…

A posteriori error estimates for a $C^1$ virtual element method applied to the thin plate vibration problem
Franco Dassi, Andres E. Rubiano, Iv\'an Vel\'asquez
https://arxiv.org/abs/2507.06846

A posteriori error estimates for a $C^1$ virtual element method applied to the thin plate vibration problem
We propose and analyse residual-based a posteriori error estimates for the virtual element discretisation applied to the thin plate vibration problem in both two and three dimensions. Our approach involves a conforming $C^1$ discrete formulation suitable for meshes composed of polygons and polyhedra. The reliability and efficiency of the error estimator are established through a dimension-independent proof. Finally, several numerical experiments are reported to demonstrate the optimal performan…

Towards a Unified View of Large Language Model Post-Training
Xingtai Lv, Yuxin Zuo, Youbang Sun, Hongyi Liu, Yuntian Wei, Zhekai Chen, Lixuan He, Xuekai Zhu, Kaiyan Zhang, Bingning Wang, Ning Ding, Bowen Zhou
https://arxiv.org/abs/2509.04419

Towards a Unified View of Large Language Model Post-Training
Two major sources of training data exist for post-training modern language models: online (model-generated rollouts) data, and offline (human or other-model demonstrations) data. These two types of data are typically used by approaches like Reinforcement Learning (RL) and Supervised Fine-Tuning (SFT), respectively. In this paper, we show that these approaches are not in contradiction, but are instances of a single optimization process. We derive a Unified Policy Gradient Estimator, and present …

KSZ Velocity Reconstruction with ACT and DESI-LS using a Tomographic QML Power Spectrum Estimator
Anderson C. M. Lai, Yurii Kvasiuk, Moritz M\"unchmeyer
https://arxiv.org/abs/2506.21684

KSZ Velocity Reconstruction with ACT and DESI-LS using a Tomographic QML Power Spectrum Estimator
We perform kinetic Sunyaev-Zel'dovich (kSZ) velocity reconstruction on data from ACT DR6 and DESI-LS DR9. To estimate the cross-power between kSZ velocity reconstruction and galaxy density, we make use of a novel quadratic maximum likelihood QML power spectrum estimator implementation in red-shift binned spherical coordinates. We find a detection of the kSZ signal from the cross-correlation between the estimated velocity field and the large-scale galaxy field of $11.7 σ$. We estimate an amplit…

Odometry Calibration and Pose Estimation of a 4WIS4WID Mobile Wall Climbing Robot
Branimir \'Caran, Vladimir Mili\'c, Marko \v{S}vaco, Bojan Jerbi\'c
https://arxiv.org/abs/2509.04016

Odometry Calibration and Pose Estimation of a 4WIS4WID Mobile Wall Climbing Robot
This paper presents the design of a pose estimator for a four wheel independent steer four wheel independent drive (4WIS4WID) wall climbing mobile robot, based on the fusion of multimodal measurements, including wheel odometry, visual odometry, and an inertial measurement unit (IMU) data using Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). The pose estimator is a critical component of wall climbing mobile robots, as their operational environment involves carrying precise measur…

Specification Tests for the Error--Law in Vector Multiplicative Errors Models
\v{S}\'arka Hudecov\'a, Simos G. Meintanis
https://arxiv.org/abs/2509.06732 https://…

Specification Tests for the Error--Law in Vector Multiplicative Errors Models
We suggest specification tests for the error distribution in vector multiplicative error models (vMEM). The test statistic is formulated as a weighted integrated distance between the parametric estimator of the Laplace transform of the null distribution and its empirical counterpart computed from the residuals. Asymptotic results are obtained under both the null and alternative hypotheses. If the Laplace transform of the null distribution is not available in closed form, we propose a test stati…

A James-Stein Estimator based Generalized OMP Algorithm for Robust Signal Recovery using Sparse Representation
Debraj Banerjee, Amitava Chatterjee
https://arxiv.org/abs/2509.01410

A James-Stein Estimator based Generalized OMP Algorithm for Robust Signal Recovery using Sparse Representation
In this paper, we introduce a novel algorithm named JS-gOMP, which enhances the generalized Orthogonal Matching Pursuit (gOMP) algorithm for improved noise robustness in sparse signal processing. The JS-gOMP algorithm uniquely incorporates the James-Stein estimator, optimizing the trade-off between signal recovery and noise suppression. This modification addresses the challenges posed by noise in the dictionary, a common issue in sparse representation scenarios. Comparative analyses demonstrate…

A difference-in-differences estimator by covariate balancing propensity score
Junjie Li, Yukitoshi Matsushita
https://arxiv.org/abs/2508.02097 https://arxi…

A difference-in-differences estimator by covariate balancing propensity score
This article develops a covariate balancing approach for the estimation of treatment effects on the treated (ATT) in a difference-in-differences (DID) research design when panel data are available. We show that the proposed covariate balancing propensity score (CBPS) DID estimator possesses several desirable properties: (i) local efficiency, (ii) double robustness in terms of consistency, (iii) double robustness in terms of inference, and (iv) faster convergence to the ATT compared to the augme…

Elliptic Interface Problem approximated by CutFEM: II. A posteriori error analysis based on equilibrated fluxes
Daniela Capatina, Aimene Gouasmi
https://arxiv.org/abs/2507.06740

Elliptic Interface Problem approximated by CutFEM: II. A posteriori error analysis based on equilibrated fluxes
This paper investigates an elliptic interface problem with discontinuous diffusion coefficients on unfitted meshes, employing the CutFEM method. The main contribution is the a posteriori error analysis based on equilibrated fluxes belonging to the immersed Raviart-Thomas space. We establish sharp reliability and local efficiency of a new error estimator, which includes both volume and interface terms, carefully tracking the dependence of the efficiency constant on the diffusion coefficients and…

Crosslisted article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
- Parametric convergence rate of a non-parametric estimator in multivariate mixtures of power serie...
Fadoua Balabdaoui, Harald Besdziek, Yong Wang

Remote Estimation for Markov Jump Linear Systems: A Distributionally Robust Approach
Ioannis Tzortzis, Themistoklis Charalambous, Charalambos D. Charalambous
https://arxiv.org/abs/2509.04116

Remote Estimation for Markov Jump Linear Systems: A Distributionally Robust Approach
This paper considers the problem of remote state estimation for Markov jump linear systems in the presence of uncertainty in the posterior mode probabilities. Such uncertainty may arise when the estimator receives noisy or incomplete measurements over an unreliable communication network. To address this challenge, the estimation problem is formulated within a distributionally robust framework, where the true posterior is assumed to lie within a total variation distance ball centered at the nomi…

Stochastic highway capacity: Unsuitable Kaplan-Meier estimator, revised maximum likelihood estimator, and impact of speed harmonisation
Igor Mikol\'a\v{s}ek
https://arxiv.org/abs/2507.00893

Stochastic highway capacity: Unsuitable Kaplan-Meier estimator, revised maximum likelihood estimator, and impact of speed harmonisation
The Kaplan-Meier estimate, also known as the product-limit method (PLM), is a widely used non-parametric maximum likelihood estimator (MLE) in survival analysis. In the context of highway engineering, it has been repeatedly applied to estimate stochastic traffic flow capacity. However, this paper demonstrates that PLM is fundamentally unsuitable for this purpose. The method implicitly assumes continuous exposure to failure risk over time - a premise invalid for traffic flow, where intensity doe…

Importance sampling for Sobol' indices estimation
Haythem Boucharif, J\'er\^ome Morio, Paul Rochet
https://arxiv.org/abs/2507.05958 https://…

Importance sampling for Sobol' indices estimation
We propose a new importance sampling framework for the estimation and analysis of Sobol' indices. We show that a Sobol' index defined under a reference input distribution can be consistently estimated from samples drawn from other sampling distributions by reweighting the estimator appropriately to account for the distribution change. We derive the optimal sampling distribution that minimizes the asymptotic variance and demonstrate its strong impact on estimation accuracy. Beyond variance reduc…

Spatial weights matrix selection and model averaging for multivariate spatial autoregressive models
Xin Miao, Fang Fang, Xuening Zhu, Hansheng Wang
https://arxiv.org/abs/2509.06005

Spatial weights matrix selection and model averaging for multivariate spatial autoregressive models
In this paper, we focus on the model specification problem in multivariate spatial econometric models when a candidate set for the spatial weights matrix is available. We propose a model selection method for the multivariate spatial autoregressive model, when the true spatial weights matrix may not be in the candidates. We show that the selected estimator is asymptotically optimal in the sense of minimizing the squared loss. If the candidate set contains the true spatial weights matrix, the met…

STable AutoCorrelation Integral Estimator (STACIE): Robust and accurate transport properties from molecular dynamics simulations
G\"ozdenur Toraman, Dieter Fauconnier, Toon Verstraelen
https://arxiv.org/abs/2506.20438

STable AutoCorrelation Integral Estimator (STACIE): Robust and accurate transport properties from molecular dynamics simulations
STACIE (STable AutoCorrelation Integral Estimator) is a novel algorithm and Python package that delivers robust, uncertainty-aware estimates of autocorrelation integrals from time-correlated data. While its primary application is deriving transport properties from equilibrium molecular dynamics simulations, STACIE is equally applicable to time-correlated data in other scientific fields. A key feature of STACIE is its ability to provide robust and accurate estimates without requiring manual adju…

Measuring the muon content of inclined air showers using AERA and the water-Cherenkov detectors of the Pierre Auger Observatory
Collaboration, Halim, Abreu, Aglietta, Allekotte, Cheminant, Almela, Aloisio, Alvarez-Mu\~niz, Ambrosone, Yebra, Anastasi, Anchordoqui, Andrada, Dourado, Andringa, Apollonio, Aramo, Arnone, Vel\'azquez, Assis, Avila, Avocone, Bakalova, Barbato, Mocellin, Bellido, Berat, Bertaina, Bianciotto, Biermann, Binet, Bismark, Bister, Biteau, Blazek, Bl\"umer, …

Measuring the muon content of inclined air showers using AERA and the water-Cherenkov detectors of the Pierre Auger Observatory
We present a novel approach for assessing the muon content of air showers with large zenith angles on a combined analysis of their radio emission and particle footprint. We use the radiation energy reconstructed by the Auger Engineering Radio Array (AERA) as an energy estimator and determine the muon number independently with the water-Cherenkov detector array of the Pierre Auger Observatory, deployed on a 1500 m grid. We focus our analysis on air showers with primary energy above 4 EeV to ensu…

Fisher Random Walk: Automatic Debiasing Contextual Preference Inference for Large Language Model Evaluation
Yichi Zhang, Alexander Belloni, Ethan X. Fang, Junwei Lu, Xiaoan Xu
https://arxiv.org/abs/2509.05852

Fisher Random Walk: Automatic Debiasing Contextual Preference Inference for Large Language Model Evaluation
Motivated by the need for rigorous and scalable evaluation of large language models, we study contextual preference inference for pairwise comparison functionals of context-dependent preference score functions across domains. Focusing on the contextual Bradley-Terry-Luce model, we develop a semiparametric efficient estimator that automates the debiased estimation through aggregating weighted residual balancing terms across the comparison graph. We show that the efficiency is achieved when the w…

Strong Consistency of the SIMEX Estimator in Linear Regression with a Conditionally Poisson Covariate
Aijun Yanga, Mary Lesperance, Farouk S. Nathoo
https://arxiv.org/abs/2509.04709

Strong Consistency of the SIMEX Estimator in Linear Regression with a Conditionally Poisson Covariate
This paper considers estimation for linear regression analysis with covariate measurement error arising from Poisson surrogates. We consider cases where covariates follow a conditional Poisson distribution, capturing non-Gaussian and heteroscedastic error structures. To address this, we extend the simulation extrapolation (SIMEX) algorithm to the conditional Poisson setting (POI-SIMEX), enabling robust adjustment in the absence of internal validation data. Theoretical analysis establishes stron…

An iterative CMB lensing estimator minimizing instrumental noise bias
Louis Legrand, Blake Sherwin, Anthony Challinor, Julien Carron, Gerrit S. Farren
https://arxiv.org/abs/2506.20667

An iterative CMB lensing estimator minimizing instrumental noise bias
Noise maps from CMB experiments are generally statistically anisotropic, due to scanning strategies, atmospheric conditions, or instrumental effects. Any mis-modeling of this complex noise can bias the reconstruction of the lensing potential and the measurement of the lensing power spectrum from the observed CMB maps. We introduce a new CMB lensing estimator based on the maximum a posteriori (MAP) reconstruction that is minimally sensitive to these instrumental noise biases. By modifying the li…

LLM as an Execution Estimator: Recovering Missing Dependency for Practical Time-travelling Debugging
Yunrui Pei, Hongshu Wang, Wenjie Zhang, Yun Lin, Weiyu Kong, Jin song Dong
https://arxiv.org/abs/2508.18721

LLM as an Execution Estimator: Recovering Missing Dependency for Practical Time-travelling Debugging
Dynamic data dependency, answering "why a variable has this value?", is critical for debugging. Given a program step `s` reading a variable `v`, finding the dynamic definition of `v` is challenging. Traditional methods require either (1) exhaustive instrumentation of all possible definitions of `v` in one run or (2) replicating the run to re-examine reads/writes - both costly. If `v` is defined in a library, instrumentation becomes expensive; for non-deterministic programs, replication is infea…

TIMEST: Temporal Information Motif Estimator Using Sampling Trees
Yunjie Pan, Omkar Bhalerao, C. Seshadhri, Nishil Talati
https://arxiv.org/abs/2507.20441 https://

TIMEST: Temporal Information Motif Estimator Using Sampling Trees
The mining of pattern subgraphs, known as motifs, is a core task in the field of graph mining. Edges in real-world networks often have timestamps, so there is a need for temporal motif mining. A temporal motif is a richer structure that imposes timing constraints on the edges of the motif. Temporal motifs have been used to analyze social networks, financial transactions, and biological networks. Motif counting in temporal graphs is particularly challenging. A graph with millions of edges can …

SAGE: A Set-based Adaptive Gradient Estimator
Lorenzo Sabug Jr., Fredy Ruiz, Lorenzo Fagiano
https://arxiv.org/abs/2508.19400 https://arxiv.org/pdf/2508.19…

SAGE: A Set-based Adaptive Gradient Estimator
A new paradigm to estimate the gradient of a black-box scalar function is introduced, considering it as a member of a set of admissible gradients that are computed using existing function samples. Results on gradient estimate accuracy, derived from a multivariate Taylor series analysis, are used to express the set of admissible gradients through linear inequalities. An approach to refine this gradient estimate set to a desired precision is proposed as well, using an adaptive sampling approach. …

CARMA: Collocation-Aware Resource Manager with GPU Memory Estimator
Ehsan Yousefzadeh-Asl-Miandoab, Reza Karimzadeh, Bulat Ibragimov, Florina M. Ciorba, P{\i}nar T\"oz\"un
https://arxiv.org/abs/2508.19073

CARMA: Collocation-Aware Resource Manager with GPU Memory Estimator
Studies conducted on enterprise-scale infrastructure have shown that GPUs -- the core computational resource for deep learning (DL) training -- are often significantly underutilized. DL task collocation on GPUs is an opportunity to address this challenge. However, it may result in (1) out-of-memory crashes for the subsequently arriving task and (2) slowdowns for all tasks sharing the GPU due to resource interference. The former challenge poses a threat to robustness, while the latter affects th…

Elliptic interface problem approximated by CutFEM: I. Conservative flux recovery and numerical validation of adaptive mesh refinement
Daniela Capatina, Aimene Gouasmi, Cuiyu He
https://arxiv.org/abs/2507.03492

Elliptic interface problem approximated by CutFEM: I. Conservative flux recovery and numerical validation of adaptive mesh refinement
We study an elliptic interface problem with discontinuous diffusion coefficients on unfitted meshes using the CutFEM method. Our main contribution is the reconstruction of conservative fluxes from the CutFEM solution and their use in a posteriori error estimation. We introduce a hybrid mixed formulation with locally computable Lagrange multipliers and reconstruct the flux in the immersed Raviart-Thomas space. Based on this, we propose a new a posteriori error estimator that includes both volume…

PMODE: Theoretically Grounded and Modular Mixture Modeling
Robert A. Vandermeulen
https://arxiv.org/abs/2508.21396 https://arxiv.org/pdf/2508.21396

PMODE: Theoretically Grounded and Modular Mixture Modeling
We introduce PMODE (Partitioned Mixture Of Density Estimators), a general and modular framework for mixture modeling with both parametric and nonparametric components. PMODE builds mixtures by partitioning the data and fitting separate estimators to each subset. It attains near-optimal rates for this estimator class and remains valid even when the mixture components come from different distribution families. As an application, we develop MV-PMODE, which scales a previously theoretical approach …

A scalable estimator of high-order information in complex dynamical systems
Alberto Liardi, George Blackburne, Hardik Rajpal, Fernando E. Rosas, Pedro A. M. Mediano
https://arxiv.org/abs/2506.18498

A scalable estimator of high-order information in complex dynamical systems
Our understanding of neural systems rests on our ability to characterise how they perform distributed computation and integrate information. Advances in information theory have introduced several quantities to describe complex information structures, where collective patterns of coordination emerge from high-order (i.e. beyond-pairwise) interdependencies. Unfortunately, the use of these approaches to study large neural systems is severely hindered by the poor scalability of existing techniques.…

On Asymptotic Analysis of the Two-Stage Approach: Towards Data-Driven Parameter Estimation
Braghadeesh Lakshminarayanan, Cristian R. Rojas
https://arxiv.org/abs/2508.18201 https…

On Asymptotic Analysis of the Two-Stage Approach: Towards Data-Driven Parameter Estimation
In this paper, we analyze the asymptotic properties of the Two-Stage (TS) estimator -- a simulation-based parameter estimation method that constructs estimators offline from synthetic data. While TS offers significant computational advantages compared to standard approaches to estimation, its statistical properties have not been previously analyzed in the literature. Under simple assumptions, we establish that the TS estimator is strongly consistent and asymptotically normal, providing the firs…

Likelihood Functions with Parameter-Dependent Support: A Survey of the Cram\'{e}r-Rao-Leibniz Lower Bound
Qin Lu, Yaakov Bar-Shalom, Peter Willett
https://arxiv.org/abs/2508.01145

Likelihood Functions with Parameter-Dependent Support: A Survey of the Cramér-Rao-Leibniz Lower Bound
Parameter estimation is a fundamental problem in science and engineering. In many safety-critical applications, one is not only interested in a {\it point} estimator, but also the uncertainty bound that can self-assess the accuracy of the estimator. In this regard, the Cramér-Rao lower bound (CRLB) is of great importance, as it provides a lower bound on the variance of {\it any} unbiased estimator. In many cases, it is the only way of evaluating, without recourse to simulations, the expected a…

Aleatoric Uncertainty from AI-based 6D Object Pose Predictors for Object-relative State Estimation
Thomas Jantos, Stephan Weiss, Jan Steinbrener
https://arxiv.org/abs/2509.01583

Aleatoric Uncertainty from AI-based 6D Object Pose Predictors for Object-relative State Estimation
Deep Learning (DL) has become essential in various robotics applications due to excelling at processing raw sensory data to extract task specific information from semantic objects. For example, vision-based object-relative navigation relies on a DL-based 6D object pose predictor to provide the relative pose between the object and the robot as measurements to the robot's state estimator. Accurately knowing the uncertainty inherent in such Deep Neural Network (DNN) based measurements is essential…

A Visibility-based 21 cm Bispectrum Estimator for Radio-interferometric Data
Sukhdeep Singh Gill, Somnath Bharadwaj
https://arxiv.org/abs/2506.10526 https:…

A Visibility-based 21 cm Bispectrum Estimator for Radio-interferometric Data
We present a fast and scalable estimator for the binned multi-frequency angular bispectrum (MABS) and the 3D bispectrum (BS) of the redshifted 21 cm signal from radio interferometric observations. The estimator operates on gridded visibilities and leverages the FFT-based acceleration to efficiently compute the MABS and the 3D BS covering all possible triangle configurations. We present the formalism and validate the estimator using simulated visibility data for a known input model BS, consideri…

Sensitivity of weighted least squares estimators to omitted variables
Leonard Wainstein, Chad Hazlett
https://arxiv.org/abs/2508.02954 https://arxiv.org/pd…

Sensitivity of weighted least squares estimators to omitted variables
This paper introduces tools for assessing the sensitivity, to unobserved confounding, of a common estimator of the causal effect of a treatment on an outcome that employs weights: the weighted linear regression of the outcome on the treatment and observed covariates. We demonstrate through the omitted variable bias framework that the bias of this estimator is a function of two intuitive sensitivity parameters: (i) the proportion of weighted variance in the treatment that unobserved confounding …

Credible Uncertainty Quantification under Noise and System Model Mismatch
Penggao Yan, Li-Ta Hsu
https://arxiv.org/abs/2509.03311 https://arxiv.org/pdf/250…

Credible Uncertainty Quantification under Noise and System Model Mismatch
State estimators often provide self-assessed uncertainty metrics, such as covariance matrices, whose reliability is critical for downstream tasks. However, these self-assessments can be misleading due to underlying modeling violations like noise or system model mismatch. This letter addresses the problem of estimator credibility by introducing a unified, multi-metric evaluation framework. We construct a compact credibility portfolio that synergistically combines traditional metrics like the Nor…

Central Limit Theorems for Transition Probabilities of Controlled Markov Chains
Ziwei Su, Imon Banerjee, Diego Klabjan
https://arxiv.org/abs/2508.01517 https://

Central Limit Theorems for Transition Probabilities of Controlled Markov Chains
We develop a central limit theorem (CLT) for the non-parametric estimator of the transition matrices in controlled Markov chains (CMCs) with finite state-action spaces. Our results establish precise conditions on the logging policy under which the estimator is asymptotically normal, and reveal settings in which no CLT can exist. We then build upon it to derive CLTs for the value, Q-, and advantage functions of any stationary stochastic policy, including the optimal policy recovered from the est…

Replaced article(s) found for econ.EM. https://arxiv.org/list/econ.EM/new
[1/1]:
- Asymptotic Properties of the Maximum Likelihood Estimator for Markov-switching Observation-driven...
Frederik Krabbe

Stochastic Taylor expansion via Poisson point processes
Weichao Wu, Athanasios C. Micheas
https://arxiv.org/abs/2508.04703 https://arxiv.org/pdf/2508.04703…

Stochastic Taylor expansion via Poisson point processes
We generalize Taylor's theorem by introducing a stochastic formulation based on an underlying Poisson point process model. We utilize this approach to propose a novel non-linear regression framework and perform statistical inference of the model parameters. Theoretical properties of the proposed estimator are also proven, including its convergence, uniformly almost surely, to the true function. The theory is presented for the univariate and multivariate cases, and we exemplify the proposed meth…

Off-Policy Learning in Large Action Spaces: Optimization Matters More Than Estimation
Imad Aouali, Otmane Sakhi
https://arxiv.org/abs/2509.03456 https://ar…

Off-Policy Learning in Large Action Spaces: Optimization Matters More Than Estimation
Off-policy evaluation (OPE) and off-policy learning (OPL) are foundational for decision-making in offline contextual bandits. Recent advances in OPL primarily optimize OPE estimators with improved statistical properties, assuming that better estimators inherently yield superior policies. Although theoretically justified, we argue this estimator-centric approach neglects a critical practical obstacle: challenging optimization landscapes. In this paper, we provide theoretical insights and extensi…

Comparison of Halo Model and Simulation Predictions for Projected-Field Kinematic Sunyaev-Zel'dovich Cross-Correlations
Michael Jacob Rodriguez, Aleksandra Kusiak, Shivam Pandey, J. Colin Hill
https://arxiv.org/abs/2509.03458

Comparison of Halo Model and Simulation Predictions for Projected-Field Kinematic Sunyaev-Zel'dovich Cross-Correlations
The kinematic Sunyaev-Zel'dovich (kSZ) effect in the cosmic microwave background (CMB) is a powerful probe of gas physics and large-scale structure (LSS) in our universe. We consider the "projected-field" kSZ estimator, which involves cross-correlating a foreground-cleaned, filtered, squared CMB temperature map with an LSS tracer, and requires no individual tracer redshifts. We compare $\verb|class_sz|$ halo model calculations of projected-field kSZ cross-correlations with measurement…

Bayes linear estimator in the general linear model
Hirai Mukasa
https://arxiv.org/abs/2506.21192 https://arxiv.org/pdf/2506.21192

Bayes linear estimator in the general linear model
Bayes linear estimators are derived by minimizing the Bayes risk with respect to the squared loss function. Non-unbiased estimators such as an ordinary ridge estimator, a typical shrinkage estimator, and a fractional rank estimator are either Bayes linear estimators or their limit points. First, we prove that Bayes linear estimators are linearly complete, but not necessarily linearly sufficient. Second, we derive a necessary and sufficient condition under which two Bayes linear estimators coinc…

Automated $h$-adaptivity for finite element approximations of the Falkner-Skan equation
B. Veena S. N. Rao
https://arxiv.org/abs/2508.02677 https://arxiv.o…

Automated $h$-adaptivity for finite element approximations of the Falkner-Skan equation
This paper details the development and application of an $h$-adaptive finite element method for the numerical solution of the \textit{Falkner-Skan equation}. A posteriori error estimation governs the adaptivity of the mesh, specifically the well-established \textit{Kelly error estimator}, which utilizes the jump in the gradient across elements. The implementation of this method allowed for accurate and efficient resolution of the boundary layer behavior characteristic of Falkner-Skan flows. Num…

Improving prediction in M-estimation by integrating external information from heterogeneous populations
Walter Dempsey, Jeremy M. G. Taylor
https://arxiv.org/abs/2509.04609 http…

Improving prediction in M-estimation by integrating external information from heterogeneous populations
A novel approach to improve prediction and inference in M-estimation by integrating external information from heterogeneous populations is proposed. Our method leverages joint asymptotics to combine estimates from external and internal datasets, where the external dataset provides auxiliary information about a subset of parameters of interest. We introduce a shrinkage estimator that combines internal and external estimates under a general class of transformations that ensure consistency across …

A Bayesian Framework For Cascaded Channel Estimation in RIS-Aided mmWave Systems
Gyoseung Lee, Junil Choi
https://arxiv.org/abs/2509.01117 https://arxiv.or…

A Bayesian Framework For Cascaded Channel Estimation in RIS-Aided mmWave Systems
In this paper, we investigate cascaded channel estimation for reconfigurable intelligent surface (RIS)-aided millimeter-wave multi-user communication systems. Since the complex channel gains of the cascaded RIS channel are generally non-Gaussian, the use of the linear minimum mean squared error (LMMSE) estimator leads to inevitable performance degradation. To tackle this issue, we propose a variational inference-based framework that approximates the complex channel gains using a complex adaptiv…

Uncertainty-Aware Ankle Exoskeleton Control
Fatima Mumtaza Tourk, Bishoy Galoaa, Sanat Shajan, Aaron J. Young, Michael Everett, Max K. Shepherd
https://arxiv.org/abs/2508.21221 …

Uncertainty-Aware Ankle Exoskeleton Control
Lower limb exoskeletons show promise to assist human movement, but their utility is limited by controllers designed for discrete, predefined actions in controlled environments, restricting their real-world applicability. We present an uncertainty-aware control framework that enables ankle exoskeletons to operate safely across diverse scenarios by automatically disengaging when encountering unfamiliar movements. Our approach uses an uncertainty estimator to classify movements as similar (in-dist…

It's Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation
Jikai Jin, Lester Mackey, Vasilis Syrgkanis
https://arxiv.org/abs/2507.02275

It's Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation
Structure-agnostic causal inference studies how well one can estimate a treatment effect given black-box machine learning estimates of nuisance functions (like the impact of confounders on treatment and outcomes). Here, we find that the answer depends in a surprising way on the distribution of the treatment noise. Focusing on the partially linear model of \citet{robinson1988root}, we first show that the widely adopted double machine learning (DML) estimator is minimax rate-optimal for Gaussian …

On the Estimation of Anisotropic Covariance Functions on Compact Two-Point Homogeneous Spaces
Alessia Caponera
https://arxiv.org/abs/2507.03723 https://

On the Estimation of Anisotropic Covariance Functions on Compact Two-Point Homogeneous Spaces
In this paper, the asymptotic theory presented in (Caponera et al., 2022) for spline-type anysotropic covariance estimator on the 2-dimensional sphere is generalized to the case of connected and compact two-point homogeneous spaces.

Analytic inference with two-way clustering
Laurent Davezies, Xavier D'Haultf{\oe}uille, Yannick Guyonvarch
https://arxiv.org/abs/2506.20749 https://

Analytic inference with two-way clustering
This paper studies analytic inference along two dimensions of clustering. In such setups, the commonly used approach has two drawbacks. First, the corresponding variance estimator is not necessarily positive. Second, inference is invalid in non-Gaussian regimes, namely when the estimator of the parameter of interest is not asymptotically Gaussian. We consider a simple fix that addresses both issues. In Gaussian regimes, the corresponding tests are asymptotically exact and equivalent to usual on…

Permutation Tests Based on the Copula-Graphic Estimator and Their Use for Survival Tree Construction
Pauline Baur, Markus Pauly, Takeshi Emura
https://arxiv.org/abs/2507.20799 h…

Permutation Tests Based on the Copula-Graphic Estimator and Their Use for Survival Tree Construction
Survival trees are popular alternatives to Cox or Aalen regression models that offer both modelling flexibility and graphical interpretability. This paper introduces a new algorithm for survival trees that relaxes the assumption of independent censoring. To this end, we use the copula-graphic estimator to estimate survival functions. This allows us to flexibly specify shape and strength of the dependence of survival and censoring times within survival trees. For splitting, we present a permutat…

Walk-on-Interfaces: A Monte Carlo Estimator for an Elliptic Interface Problem with Nonhomogeneous Flux Jump Conditions and a Neumann Boundary Condition
Xinwen Ding, Adam R Stinchcombe
https://arxiv.org/abs/2508.16767

Walk-on-Interfaces: A Monte Carlo Estimator for an Elliptic Interface Problem with Nonhomogeneous Flux Jump Conditions and a Neumann Boundary Condition
Elliptic interface problems arise in numerous scientific and engineering applications, modeling heterogeneous materials in which physical properties change discontinuously across interfaces. In this paper, we present \textit{Walk-on-Interfaces} (WoI), a grid-free Monte Carlo estimator for a class of Neumann elliptic interface problems with nonhomogeneous flux jump conditions. Our Monte Carlo estimators maintain consistent accuracy throughout the domain and, thus, do not suffer from the well-kno…

Optimal Estimation for General Gaussian Processes
Tetsuya Takabatake, Jun Yu, Chen Zhang
https://arxiv.org/abs/2509.04987 https://arxiv.org/pdf/2509.04987

Optimal Estimation for General Gaussian Processes
This paper proposes a novel exact maximum likelihood (ML) estimation method for general Gaussian processes, where all parameters are estimated jointly. The exact ML estimator (MLE) is consistent and asymptotically normally distributed. We prove the local asymptotic normality (LAN) property of the sequence of statistical experiments for general Gaussian processes in the sense of Le Cam, thereby enabling optimal estimation and facilitating statistical inference. The results rely solely on the asy…

MoCap2GT: A High-Precision Ground Truth Estimator for SLAM Benchmarking Based on Motion Capture and IMU Fusion
Zichao Shu, Shitao Bei, Jicheng Dai, Lijun Li, Zetao Chen
https://arxiv.org/abs/2507.12920

MoCap2GT: A High-Precision Ground Truth Estimator for SLAM Benchmarking Based on Motion Capture and IMU Fusion
Marker-based optical motion capture (MoCap) systems are widely used to provide ground truth (GT) trajectories for benchmarking SLAM algorithms. However, the accuracy of MoCap-based GT trajectories is mainly affected by two factors: spatiotemporal calibration errors between the MoCap system and the device under test (DUT), and inherent MoCap jitter. Consequently, existing benchmarks focus primarily on absolute translation error, as accurate assessment of rotation and inter-frame errors remains c…

Quadratic estimators for unwindowed power spectrum of galaxy-galaxy weak lensing and its application to $P_{\rm gm}(k)$ estimation
Taisei Terawaki, Masahiro Takada, Takanori Taniguchi
https://arxiv.org/abs/2507.18789

Quadratic estimators for unwindowed power spectrum of galaxy-galaxy weak lensing and its application to $P_{\rm gm}(k)$ estimation
Galaxy-galaxy weak lensing provides a powerful means of measuring the average matter distribution around lens galaxies -- i.e., the galaxy bias relation. Properly accounting for the spin-2 nature of weak lensing distortions, we develop a quadratic estimator for measuring the $E$- and $B$-mode angular power spectra from galaxy-galaxy weak lensing, correcting for survey window effects arising from, for example, survey geometry and bright star masks. The estimator can be implemented efficiently by…

A Deep Learning based Signal Dimension Estimator with Single Snapshot Signal in Phased Array Radar Application
Yugang Ma, Yonghong Zeng, Sumei Sun, Gary Lee, Ernest Kurniawan, Francois Chin Po Shin
https://arxiv.org/abs/2508.10263

A Deep Learning based Signal Dimension Estimator with Single Snapshot Signal in Phased Array Radar Application
Signal dimension, defined here as the number of copies with different delays or angular shifts, is a prerequisite for many high-resolution delay estimation and direction-finding algorithms in sensing and communication systems. Thus, correctly estimating signal dimension itself becomes crucial. In this paper, we present a deep learning-based signal dimension estimator (DLSDE) with single-snapshot observation in the example application of phased array radar. Unlike traditional model-based and exi…

Nonparametric Estimation from Correlated Copies of a Drifted 2nd-Order Process
Nicolas Marie
https://arxiv.org/abs/2508.05259 https://arxiv.org/pdf/2508.05…

Nonparametric Estimation from Correlated Copies of a Drifted 2nd-Order Process
This paper presents several situations leading to the observation of multiple - possibly correlated - copies of a drifted 2nd-order process, and then non-asymptotic risk bounds are established on nonparametric estimators of the drift function $b_0$ and its derivative. For drifted Gaussian processes with a regular enough covariance function, a sharper risk bound is established on the estimator of $b_0'$, and a model selection procedure is provided with theoretical guarantees.

Estimation in linear models with clustered data
Anna Mikusheva, Mikkel S{\o}lvsten, Baiyun Jing
https://arxiv.org/abs/2508.12860 https://arxiv.org/pdf/2508…

Estimation in linear models with clustered data
We study linear regression models with clustered data, high-dimensional controls, and a complicated structure of exclusion restrictions. We propose a correctly centered internal IV estimator that accommodates a variety of exclusion restrictions and permits within-cluster dependence. The estimator has a simple leave-out interpretation and remains computationally tractable. We derive a central limit theorem for its quadratic form and propose a robust variance estimator. We also develop inference …

A posteriori error estimates and space-adaptive mesh refinements for time-dependent scattering problems
Th\'eophile Chaumont-Frelet, Heiko Gimperlein, Ignacio Labarca-Figueroa, J\"org Nick
https://arxiv.org/abs/2509.04217

A posteriori error estimates and space-adaptive mesh refinements for time-dependent scattering problems
This work studies a posteriori error estimates and their use for time-dependent acoustic scattering problems, formulated as a time-dependent boundary integral equation based on a single-layer ansatz. The integral equation is discretized by the convolution quadrature method in time and by boundary elements in space. We prove the reliability of an error estimator of residual type and study the resulting space-adaptive mesh refinements. Moreover, we present a simple modification of the convolution…

Nearly Minimax Discrete Distribution Estimation in Kullback-Leibler Divergence with High Probability
Dirk van der Hoeven, Julia Olkhovskaia, Tim van Erven
https://arxiv.org/abs/2507.17316

Nearly Minimax Discrete Distribution Estimation in Kullback-Leibler Divergence with High Probability
We consider the problem of estimating a discrete distribution $p$ with support of size $K$ and provide both upper and lower bounds with high probability in KL divergence. We prove that in the worst case, for any estimator $\widehat{p}$, with probability at least $δ$, $\text{KL}(p \| \widehat{p}) \geq C\max\{K,\ln(K)\ln(1/δ) \}/n $, where $n$ is the sample size and $C > 0$ is a constant. We introduce a computationally efficient estimator $p^{\text{OTB}}$, based on Online to Batch conversi…

Triply Robust Panel Estimators
Susan Athey, Guido Imbens, Zhaonan Qu, Davide Viviano
https://arxiv.org/abs/2508.21536 https://arxiv.org/pdf/2508.21536

Triply Robust Panel Estimators
This paper studies estimation of causal effects in a panel data setting. We introduce a new estimator, the Triply RObust Panel (TROP) estimator, that combines $(i)$ a flexible model for the potential outcomes based on a low-rank factor structure on top of a two-way-fixed effect specification, with $(ii)$ unit weights intended to upweight units similar to the treated units and $(iii)$ time weights intended to upweight time periods close to the treated time periods. We study the performance of th…

Testing gravitational physics by combining DESI DR1 and weak lensing datasets using the E_G estimator
S. J. Rauhut, C. Blake, U. Andrade, H. E. Noriega, J. Aguilar, S. Ahlen, S. BenZvi, D. Bianchi, D. Brooks, T. Claybaugh, A. Cuceu, A. de la Macorra, J. DeRose, P. Doel, N. Emas, S. Ferraro, J. E. Forero-Romero, C. Garcia-Quintero, E. Gazta\~naga, G. Gutierrez, S. Heydenreich, K. Honscheid, C. Howlett, D. Huterer, M. Ishak, S. Joudaki, R. Joyce, E. Jullo, R. Kehoe, D. Kirkby, A. Kremin,…

Testing gravitational physics by combining DESI DR1 and weak lensing datasets using the E_G estimator
The action of gravitational physics across space-time creates observable signatures in the behaviour of light and matter. We perform combined-probe studies using data from the Baryon Oscillation Spectroscopic Survey (BOSS) and Dark Energy Spectroscopic Instrument survey Data Release 1 (DESI-DR1), in combination with three existing weak lensing surveys, to test and constrain General Relativity (GR) in the context of the standard model of cosmology (LCDM). We focus on measuring the gravitational …

Convergence of empirical Gromov-Wasserstein distance
Kengo Kato, Boyu Wang
https://arxiv.org/abs/2508.03985 https://arxiv.org/pdf/2508.03985

Convergence of empirical Gromov-Wasserstein distance
We study rates of convergence for estimation of the Gromov-Wasserstein distance. For two marginals supported on compact subsets of $\R^{d_x}$ and $\R^{d_y}$, respectively, with $\min \{ d_x,d_y \} > 4$, prior work established the rate $n^{-\frac{2}{\min\{d_x,d_y\}}}$ for the plug-in empirical estimator based on $n$ i.i.d. samples. We extend this fundamental result to marginals with unbounded supports, assuming only finite polynomial moments. Our proof techniques for the upper bounds can be adap…

Local empirical Bayes correction for Bayesian modeling
Yoshiko Hayashi
https://arxiv.org/abs/2506.11424 https://arxiv.org/pdf/2506.11…

Local empirical Bayes correction for Bayesian modeling
The James-Stein estimator has attracted much interest as a shrinkage estimator that yields better estimates than the maximum likelihood estimator. The James-Stein estimator is also very useful as an argument in favor of empirical Bayesian methods. However, for problems involving large-scale data, such as differential gene expression data, the distribution is considered a mixture distribution with different means that cannot be considered sufficiently close. Therefore, it is not appropriate to a…

A Better Linear Unbiased Estimator for Averages over Discrete Structures
Bastiaan J. Braams
https://arxiv.org/abs/2507.19294 https://arxiv.org/pdf/2507.192…

A Better Linear Unbiased Estimator for Averages over Discrete Structures
Given an i.i.d. sample drawn from some probability distribution on a finite set, the best (in the sense of least variance) linear unbiased estimator (BLUE) of the average of any quantity with respect to that distribution is the sample average of the quantity. Here we consider the situation in which, together with the sample, also the probability mass (possibly unnormalized) at each sample point is provided. We show that with that information BLUE can be systematically improved. The proposed pro…

High dimensional convergence rates for sparse precision estimators for matrix-variate data
Hongqiang Sun, Kshitij Khare
https://arxiv.org/abs/2507.01741 ht…

High dimensional convergence rates for sparse precision estimators for matrix-variate data
In several applications, the underlying structure of the data allows for the samples to be organized into a matrix variate form. In such settings, the underlying row and column covariance matrices are fundamental quantities of interest. We focus our attention on two popular estimators that have been proposed in the literature: a penalized sparse estimator called SMGM and a heuristic sample covariance estimator. We establish convergence rates for these estimators in relevant high-dimensional set…

A posteriori and a priori error estimates for linearized thin sheet folding
Harbir Antil, Sean P. Carney, Rohit Khandelwal
https://arxiv.org/abs/2507.00807

A posteriori and a priori error estimates for linearized thin sheet folding
We describe a posteriori error analysis for a discontinuous Galerkin method for a fourth order elliptic interface problem that arises from a linearized model of thin sheet folding. The primary contribution is a local efficiency bound for an estimator that measures the extent to which the interface conditions along the fold are satisfied, which is accomplished by constructing a novel edge bubble function. We subsequently conduct a medius analysis to obtain improved a priori error estimates under…

A penalized least squares estimator for extreme-value mixture models
Anas Mourahib, Anna Kiriliouk, Johan Segers
https://arxiv.org/abs/2506.15272 https://

A penalized least squares estimator for extreme-value mixture models
Estimating the parameters of max-stable parametric models poses significant challenges, particularly when some parameters lie on the boundary of the parameter space. This situation arises when a subset of variables exhibits extreme values simultaneously, while the remaining variables do not -- a phenomenon referred to as an extreme direction in the literature. In this paper, we propose a novel estimator for the parameters of a general parametric mixture model, incorporating a penalization appro…

Bias Correction in Factor-Augmented Regression Models with Weak Factors
Peiyun Jiang, Yoshimasa Uematsu, Takashi Yamagata
https://arxiv.org/abs/2509.02066 https://

Bias Correction in Factor-Augmented Regression Models with Weak Factors
In this paper, we study the asymptotic bias of the factor-augmented regression estimator and its reduction, which is augmented by the $r$ factors extracted from a large number of $N$ variables with $T$ observations. In particular, we consider general weak latent factor models with $r$ signal eigenvalues that may diverge at different rates, $N^{α_{k}}$, $0<α_{k}\leq 1$, $k=1,\dots,r$. In the existing literature, the bias has been derived using an approximation for the estimated factors with a …

Asymptotic breakdown point analysis of the minimum density power divergence estimator under independent non-homogeneous setups
Suryasis Jana, Subhrajyoty Roy, Ayanendranath Basu, Abhik Ghosh
https://arxiv.org/abs/2508.12426

Asymptotic breakdown point analysis of the minimum density power divergence estimator under independent non-homogeneous setups
The minimum density power divergence estimator (MDPDE) has gained significant attention in the literature of robust inference due to its strong robustness properties and high asymptotic efficiency; it is relatively easy to compute and can be interpreted as a generalization of the classical maximum likelihood estimator. It has been successfully applied in various setups, including the case of independent and non-homogeneous (INH) observations that cover both classification and regression-type pr…

A Minimum Distance Estimator Approach for Misspecified Ergodic Processes
Jaroslav I. Borodavka, Sebastian Krumscheid, Grigorios A. Pavliotis
https://arxiv.org/abs/2506.12432

A Minimum Distance Estimator Approach for Misspecified Ergodic Processes
We propose a minimum distance estimator (MDE) for parameter identification in misspecified models characterized by a sequence of ergodic stochastic processes that converge weakly to the model of interest. The data is generated by the sequence of processes, and we are interested in inferring parameters for the limiting processes. We define a general statistical setting for parameter estimation under such model misspecification and prove the robustness of the MDE. Furthermore, we prove the asympt…

Causal Spatial Quantile Regression
Yan Gong, Reetam Majumder, Brian J. Reich, Rapha\"el Huser
https://arxiv.org/abs/2509.02294 https://arxiv.org/pdf/2…

Causal Spatial Quantile Regression
Treatment effects in a wide range of economic, environmental, and epidemiological applications often vary across space, and understanding the heterogeneity of causal effects across space and outcome quantiles is a critical challenge in spatial causal inference. To effectively capture spatial heterogeneity in distributional treatment effects, we propose a novel semiparametric neural network-based causal framework leveraging deep spatial quantile regression and then construct a plug-in estimator …

Replaced article(s) found for math.ST. https://arxiv.org/list/math.ST/new
[1/1]:
- The Poisson tensor completion non-parametric differential entropy estimator
Daniel M. Dunlavy, Richard B. Lehoucq, Carolyn D. Mayer, Arvind Prasadan

Robust Survival Estimation under Interval Censoring: Expectation-Maximization and Bayesian Accelerated Failure Time Assessment via Simulation and Application
J. T. Korley
https://arxiv.org/abs/2509.02634

Robust Survival Estimation under Interval Censoring: Expectation-Maximization and Bayesian Accelerated Failure Time Assessment via Simulation and Application
Interval censoring occurs when event times are only known to fall between scheduled assessments, a common design in clinical trials, epidemiology, and reliability studies. Standard right-censoring methods, such as Kaplan-Meier and Cox regression, are not directly applicable and can produce biased results. This study compares three complementary approaches for interval-censored survival data. First, the Turnbull nonparametric maximum likelihood estimator (NPMLE) via the EM algorithm recovers the…

On the Bernstein-smoothed lower-tail Spearman's rho estimator
Fr\'ed\'eric Ouimet, Selim Orhun Susam
https://arxiv.org/abs/2506.08857 https://

On the Bernstein-smoothed lower-tail Spearman's rho estimator
This note develops a Bernstein estimator for lower-tail Spearman's rho and establishes its strong consistency and asymptotic normality under mild regularity conditions. Smoothing the empirical copula yields a strictly smaller mean squared error (MSE) in tail regions by lowering sampling variance relative to the classical Spearman's rho estimator. A Monte Carlo simulation experiment with the Farlie--Gumbel--Morgenstern copula demonstrates variance reductions that translate into lower MSE estimat…

Nonparametric regression for a circular response with error-in-covariate
Nicholas Woolsey, Xianzheng Huang
https://arxiv.org/abs/2508.15962 https://arxiv.o…

Nonparametric regression for a circular response with error-in-covariate
This study considers regression analysis of a circular response with an error-prone linear covariate. Starting with an existing estimator of the circular regression function that assumes error-free covariate, three approaches are proposed to revise this estimator, leading to three nonparametric estimators for the circular regression function accounting for measurement error. The proposed estimators are intrinsically connected through some deconvoluting operator that is exploited differently in …

Asymptotic confidence bands for the histogram regression estimator
Natalie Neumeyer, Jan Rabe, Mathias Trabs
https://arxiv.org/abs/2508.12391 https://arxiv…

Asymptotic confidence bands for the histogram regression estimator
In a multivariate nonparametric regression model with a Hölder continuous regression function and heteroscedastic noise asymptotic uniform confidence bands are constructed based on the histogram estimator. The radius of the confidence bands does not depend on an extreme value distribution, but instead can be numerically calculated for the chosen binning.

Differential Distance Correlation and Its Applications
Yixiao Liu, Pengjian Shang
https://arxiv.org/abs/2507.00524 https://arxiv.org/…

Differential Distance Correlation and Its Applications
In this paper, we propose a novel coefficient, named differential distance correlation, to measure the strength of dependence between a random variable $ Y \in \mathbb {R} $ and a random vector $ X \in \mathbb {R}^{p} $. The coefficient has a concise expression and is invariant to arbitrary orthogonal transformations of the random vector. Moreover, the coefficient is a strongly consistent estimator of a simple and interpretable dependent measure, which is 0 if and only if $ X $ and $ Y $ are in…

Two-Sample Covariance Inference in High-Dimensional Elliptical Models
Nina D\"ornemann
https://arxiv.org/abs/2507.02640 https://…

Two-Sample Covariance Inference in High-Dimensional Elliptical Models
We propose a two-sample test for large-dimensional covariance matrices in generalized elliptical models. The test statistic is based on a U-statistic estimator of the squared Frobenius norm of the difference between the two population covariance matrices. This statistic was originally introduced by Li and Chen (2012, AoS) for the independent component model. As a key theoretical contribution, we establish a new central limit theorem for the U-statistics under elliptical data, valid under both t…

Robust Tail Index Estimation under Random Censoring via Minimum Density Power Divergence
Nour Elhouda Guesmia, Abdelhakim Necir, Djamel Meraghni
https://arxiv.org/abs/2507.18737

Robust Tail Index Estimation under Random Censoring via Minimum Density Power Divergence
Based on the minimum density power divergence approach, we propose a robust estimator of the tail index for randomly right-censored data from a Pareto-type distribution. We establish its consistency and asymptotic normality. An extensive simulation study is performed to assess the finite-sample behavior of the estimator in comparison with existing ones. The methodology is further illustrated through an application to a real AIDS survival dataset.

Upgrading survival models with CARE
William G. Underwood, Henry W. J. Reeve, Oliver Y. Feng, Samuel A. Lambert, Bhramar Mukherjee, Richard J. Samworth
https://arxiv.org/abs/2506.23870

Upgrading survival models with CARE
Clinical risk prediction models are regularly updated as new data, often with additional covariates, become available. We propose CARE (Convex Aggregation of relative Risk Estimators) as a general approach for combining existing "external" estimators with a new data set in a time-to-event survival analysis setting. Our method initially employs the new data to fit a flexible family of reproducing kernel estimators via penalised partial likelihood maximisation. The final relative risk estimator i…

Parametric convergence rate of some nonparametric estimators in mixtures of power series distributions
Fadoua Balabdaoui, Harald Besdziek, Yong Wang
https://arxiv.org/abs/2508.00163

Parametric convergence rate of some nonparametric estimators in mixtures of power series distributions
We consider the problem of estimating a mixture of power series distributions with infinite support, to which belong very well-known models such as Poisson, Geometric, Logarithmic or Negative Binomial probability mass functions. We consider the nonparametric maximum likelihood estimator (NPMLE) and show that, under very mild assumptions, it converges to the true mixture distribution $π_0$ at a rate no slower than $(\log n)^{3/2} n^{-1/2}$ in the Hellinger distance. Recent work on minimax lower…