Tootfinder

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…

Logarithmic Accuracy in Importance Sampling via Large Deviations
Julie Choi, Peter Glynn
https://arxiv.org/abs/2509.00343 https://arxiv.org/pdf/2509.00343

Logarithmic Accuracy in Importance Sampling via Large Deviations
Importance sampling (IS) is a widely used simulation method for estimating rare event probabilities. In IS, the relative variance of an estimator is the most common measure of estimator accuracy, and the focus of existing literature is on constructing an importance measure under which the relative variance of the estimator grows sub-exponentially as the parameter increases. In practice, constructing such an estimator is not easy. In this work, we study the behavior of IS estimators under an imp…

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…

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…

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…

Estimating a regression function under possible heteroscedastic and heavy-tailed errors. Application to shape-restricted regression
Yannick Baraud, Guillaume Maillard
https://arxiv.org/abs/2506.00852

Estimating a regression function under possible heteroscedastic and heavy-tailed errors. Application to shape-restricted regression
We consider a regression framework where the design points are deterministic and the errors possibly non-i.i.d. and heavy-tailed (with a moment of order $p$ in $[1,2]$). Given a class of candidate regression functions, we propose a surrogate for the classical least squares estimator (LSE). For this new estimator, we establish a nonasymptotic risk bound with respect to the absolute loss which takes the form of an oracle type inequality. This inequality shows that our estimator possesses natural …

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…

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…

Robust Inference when Nuisance Parameters may be Partially Identified with Applications to Synthetic Controls
Joseph Fry
https://arxiv.org/abs/2507.00307 h…

Robust Inference when Nuisance Parameters may be Partially Identified with Applications to Synthetic Controls
When conducting inference for the average treatment effect on the treated with a Synthetic Control Estimator, the vector of control weights is a nuisance parameter which is often constrained, high-dimensional, and may be only partially identified even when the average treatment effect on the treated is point-identified. All three of these features of a nuisance parameter can lead to failure of asymptotic normality for the estimate of the parameter of interest when using standard methods. I prov…

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 …

Scale-Dependent Multifractality in Bitcoin Realised Volatility: Implications for Rough Volatility Modelling
Milan Pontiggia (MAGEFI - University of Bordeaux, France)
https://arxiv.org/abs/2507.00575

Scale-Dependent Multifractality in Bitcoin Realised Volatility: Implications for Rough Volatility Modelling
We assess the applicability of rough volatility models to Bitcoin realised volatility using the normalised p-variation framework of Cont and Das (2024). Applying this model free estimator to high-frequency Bitcoin data from 2017 to 2024 across multiple sampling resolutions, we find that the normalised statistic remains strictly negative throughout, precluding the estimation of a valid roughness index. Stationarity tests and robustness checks reveal no significant evidence of non-stationarity or…

Flying Co-Stereo: Enabling Long-Range Aerial Dense Mapping via Collaborative Stereo Vision of Dynamic-Baseline
Zhaoying Wang, Xingxing Zuo, Wei Dong
https://arxiv.org/abs/2506.00546

Flying Co-Stereo: Enabling Long-Range Aerial Dense Mapping via Collaborative Stereo Vision of Dynamic-Baseline
Lightweight long-range mapping is critical for safe navigation of UAV swarms in large-scale unknown environments. Traditional stereo vision systems with fixed short baselines face limited perception ranges. To address this, we propose Flying Co-Stereo, a cross-agent collaborative stereo vision system that leverages the wide-baseline spatial configuration of two UAVs for long-range dense mapping. Key innovations include: (1) a dual-spectrum visual-inertial-ranging estimator for robust baseline e…

A Quantized Order Estimator
Lida Jing
https://arxiv.org/abs/2506.00984 https://arxiv.org/pdf/2506.00984

A Quantized Order Estimator
This paper considers the order estimation problem of stochastic autoregressive exogenous input (ARX) systems by using quantized data. Based on the least squares algorithm and inspired by the control systems information criterion (CIC), a new kind of criterion aimed at addressing the inaccuracy of quantized data is proposed for ARX systems with quantized data. When the upper bounds of the system orders are known and the persistent excitation condition is satisfied, the system order estimates are…

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 …

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…

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 …

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…

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. …

Uniform Validity of the Subset Anderson-Rubin Test under Heteroskedasticity and Nonlinearity
Atsushi Inoue, \`Oscar Jord\`a, Guido M. Kuersteiner
https://arxiv.org/abs/2507.01167 …

Uniform Validity of the Subset Anderson-Rubin Test under Heteroskedasticity and Nonlinearity
We consider the Anderson-Rubin (AR) statistic for a general set of nonlinear moment restrictions. The statistic is based on the criterion function of the continuous updating estimator (CUE) for a subset of parameters not constrained under the Null. We treat the data distribution nonparametrically with parametric moment restrictions imposed under the Null. We show that subset tests and confidence intervals based on the AR statistic are uniformly valid over a wide range of distributions that incl…

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…

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…

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

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…

Discrete Fourier Transform versus Discrete Chi-Square Method
Lauri Jetsu
https://arxiv.org/abs/2509.01540 https://arxiv.org/pdf/2509.01540

Discrete Fourier Transform versus Discrete Chi-Square Method
We compare two time series analysis methods, the Discrete Fourier Transform (DFT) and our Discrete Chi-square Method (DCM). DCM is designed for detecting many signals superimposed on an unknown trend. The solution for the non-linear DCM model is an ill-posed problem. The backbone of DCM is the Gauss-Markov theorem that the least squares fit is the best unbiased estimator for linear regression models. DCM is a simple numerical time series analysis method that performs a massive number of linear …

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.…

Regularity and propagation of chaos for conditional McKean-Vlasov equations
Manuel Arnese
https://arxiv.org/abs/2507.22222 https://arxiv.org/pdf/2507.22222…

Regularity and propagation of chaos for conditional McKean-Vlasov equations
We study the rate of propagation of chaos for a McKea--Vlasov equation with conditional expectation terms in the drift. We use a (regularized) Nadaraya-Watson estimator at a particle level to approximate the conditional expectations; we then combine relative entropy methods in the spirit of Jabin and Wang (2018) with information theoretic inequalities to obtain the result. The nonparametric nature of the problem requires higher regularity for the density of the McKean-Vlasov limit, which we obt…

Handling Sparse Non-negative Data in Finance
Agostino Capponi, Zhaonan Qu
https://arxiv.org/abs/2509.01478 https://arxiv.org/pdf/2509.01478

Handling Sparse Non-negative Data in Finance
We show that Poisson regression, though often recommended over log-linear regression for modeling count and other non-negative variables in finance and economics, can be far from optimal when heteroskedasticity and sparsity -- two common features of such data -- are both present. We propose a general class of moment estimators, encompassing Poisson regression, that balances the bias-variance trade-off under these conditions. A simple cross-validation procedure selects the optimal estimator. Num…

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…

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…

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 …

Empirical estimator of diversification quotient
Xia Han, Liyuan Lin, Mengshi Zhao
https://arxiv.org/abs/2506.20385 https://arxiv.org/…

Empirical estimator of diversification quotient
The Diversification Quotient (DQ), introduced by Han et al. (2025), is a recently proposed measure of portfolio diversification that quantifies the reduction in a portfolio's risk-level parameter attributable to diversification. Grounded in a rigorous theoretical framework, DQ effectively captures heavy tails, common shocks, and enhances efficiency in portfolio optimization. This paper further explores the convergence properties and asymptotic normality of empirical DQ estimators based on Value…

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…

A new family of a posteriori error estimates for non-conforming finite element methods leading to stabilization-free error bounds
T. Chaumont-Frelet
https://arxiv.org/abs/2506.23381

A new family of a posteriori error estimates for non-conforming finite element methods leading to stabilization-free error bounds
We propose new a posteriori error estimators for non-conforming finite element discretizations of second-order elliptic PDE problems. These estimators are based on novel reformulations of the standard Prager-Synge identity, and enable to prove efficiency estimates without extra stabilization terms in the error measure for a large class of discretization schemes. We propose a residual-based estimator for which the efficiency constant scales optimally in polynomial degree, as well as two equilibr…

Replaced article(s) found for cs.CR. https://arxiv.org/list/cs.CR/new
[1/1]:
- Improving Count-Mean Sketch as the Leading Locally Differentially Private Frequency Estimator for...
Mingen Pan

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…

Learning Binary Sampling Patterns for Single-Pixel Imaging using Bilevel Optimisation
Serban C. Tudosie, Alexander Denker, Zeljko Kereta, Simon Arridge
https://arxiv.org/abs/2508.19068

Learning Binary Sampling Patterns for Single-Pixel Imaging using Bilevel Optimisation
Single-Pixel Imaging enables reconstructing objects using a single detector through sequential illuminations with structured light patterns. We propose a bilevel optimisation method for learning task-specific, binary illumination patterns, optimised for applications like single-pixel fluorescence microscopy. We address the non-differentiable nature of binary pattern optimisation using the Straight-Through Estimator and leveraging a Total Deep Variation regulariser in the bilevel formulation. We…

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…

A novel stratified sampler with unbalanced refinement for network reliability assessment
Jianpeng Chan, Iason Papaioannou, Daniel Straub
https://arxiv.org/abs/2506.01044

A novel stratified sampler with unbalanced refinement for network reliability assessment
We investigate stratified sampling in the context of network reliability assessment. We propose an unbalanced stratum refinement procedure, which operates on a partition of network components into clusters and the number of failed components within each cluster. The size of each refined stratum and the associated conditional failure probability, collectively termed failure signatures, can be calculated and estimated using the conditional Bernoulli model. The estimator is further improved by det…

A note on a resampling procedure for estimating the density at a given quantile
Beatriz Farah (MAP5 - UMR 8145), Aur\'elien Latouche (CNAM Paris), Olivier Bouaziz (LPP, MAP5 - UMR 8145)
https://arxiv.org/abs/2509.02207

A note on a resampling procedure for estimating the density at a given quantile
In this paper we refine the procedure proposed by Lin et al. (2015) to estimate the density at a given quantile based on a resampling method. The approach consists on generating multiple samples of the zero-mean Gaussian variable from which a least square estimator is constructed. The main advantage of the proposed method is that it provides an estimation directly at the quantile of interest, thus achieving the parametric rate of convergence. In this study, we investigate the critical role of t…

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…

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…

This https://arxiv.org/abs/2405.05969 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Learned harmonic mean estimation of the Bayesian evidence with normalizing flows
We present the learned harmonic mean estimator with normalizing flows - a robust, scalable and flexible estimator of the Bayesian evidence for model comparison. Since the estimator is agnostic to sampling strategy and simply requires posterior samples, it can be applied to compute the evidence using any Markov chain Monte Carlo (MCMC) sampling technique, including saved down MCMC chains, or any variational inference approach. The learned harmonic mean estimator was recently introduced, where ma…

Re-weighting estimator for ab initio path integral Monte Carlo simulations of fictitious identical particles
Tobias Dornheim, Pontus Svensson, Paul Hamann, Sebastian Schwalbe, Zhandos Moldabekov, Panagiotis Tolias, Jan Vorberger
https://arxiv.org/abs/2508.12323

Re-weighting estimator for ab initio path integral Monte Carlo simulations of fictitious identical particles
The fermion sign problem constitutes one of the most fundamental obstacles in quantum many-body theory. Recently, it has been suggested to circumvent the sign problem by carrying out path integral simulations with a fictitious quantum statistics variable $ξ$, which allows for a smooth interpolation between the bosonic and fermionic limits [\textit{J.~Chem.~Phys.}~\textbf{157}, 094112 (2022)]. This $ξ$-extrapolation method has subsequently been applied to a variety of systems and has facilitat…

Crosslisted article(s) found for cs.PF. https://arxiv.org/list/cs.PF/new
[1/1]:
- CARMA: Collocation-Aware Resource Manager with GPU Memory Estimator
Yousefzadeh-Asl-Miandoab, Karimzadeh, Ibragimov, Ciorba, T\un

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…

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…

Enhancing Differentially Private Linear Regression via Public Second-Moment
Zilong Cao (The School of Mathematics, Northwest University), Hai Zhang (The School of Mathematics, Northwest University)
https://arxiv.org/abs/2508.18037

Enhancing Differentially Private Linear Regression via Public Second-Moment
Leveraging information from public data has become increasingly crucial in enhancing the utility of differentially private (DP) methods. Traditional DP approaches often require adding noise based solely on private data, which can significantly degrade utility. In this paper, we address this limitation in the context of the ordinary least squares estimator (OLSE) of linear regression based on sufficient statistics perturbation (SSP) under the unbounded data assumption. We propose a novel method …

Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives
Brian Liu, Rahul Mazumder, Peter Radchenko
https://arxiv.org/abs/2506.20114

Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives
Tree ensembles are non-parametric methods widely recognized for their accuracy and ability to capture complex interactions. While these models excel at prediction, they are difficult to interpret and may fail to uncover useful relationships in the data. We propose an estimator to extract compact sets of decision rules from tree ensembles. The extracted models are accurate and can be manually examined to reveal relationships between the predictors and the response. A key novelty of our estimator…

Optimal Estimation of Two-Way Effects under Limited Mobility
Xu Cheng, Sheng Chao Ho, Frank Schorfheide
https://arxiv.org/abs/2506.21987 https://

Optimal Estimation of Two-Way Effects under Limited Mobility
We propose an empirical Bayes estimator for two-way effects in linked data sets based on a novel prior that leverages patterns of assortative matching observed in the data. To capture limited mobility we model the bipartite graph associated with the matched data in an asymptotic framework where its Laplacian matrix has small eigenvalues that converge to zero. The prior hyperparameters that control the shrinkage are determined by minimizing an unbiased risk estimate. We show the proposed empiric…

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…

On the Role of Age and Semantics of Information in Remote Estimation of Markov Sources
Jiping Luo, Nikolaos Pappas
https://arxiv.org/abs/2507.18514 https://

On the Role of Age and Semantics of Information in Remote Estimation of Markov Sources
This paper investigates the semantics-aware remote estimation of a finite-state Markov chain. We employ the maximum a posteriori (MAP) estimator and aim to devise a transmission policy to optimize estimation performance subject to a transmission frequency constraint. We leverage two metrics, namely the Age of Consecutive Error (AoCE) and the Age of Information (AoI), to quantify, respectively, the significance of estimation error at the transmitter and the predictability of outdated information…

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…

Spatial Language Likelihood Grounding Network for Bayesian Fusion of Human-Robot Observations
Supawich Sitdhipol, Waritwong Sukprasongdee, Ekapol Chuangsuwanich, Rina Tse
https://arxiv.org/abs/2507.19947

Spatial Language Likelihood Grounding Network for Bayesian Fusion of Human-Robot Observations
Fusing information from human observations can help robots overcome sensing limitations in collaborative tasks. However, an uncertainty-aware fusion framework requires a grounded likelihood representing the uncertainty of human inputs. This paper presents a Feature Pyramid Likelihood Grounding Network (FP-LGN) that grounds spatial language by learning relevant map image features and their relationships with spatial relation semantics. The model is trained as a probability estimator to capture a…

Online Algorithms for Recovery of Low-Rank Parameter Matrix in Non-stationary Stochastic Systems
Yanxin Fu, Junbao Zhou, Yu Hu, Wenxiao Zhao
https://arxiv.org/abs/2506.19299

Online Algorithms for Recovery of Low-Rank Parameter Matrix in Non-stationary Stochastic Systems
This paper presents a two-stage online algorithm for recovery of low-rank parameter matrix in non-stationary stochastic systems. The first stage applies the recursive least squares (RLS) estimator combined with its singular value decomposition to estimate the unknown parameter matrix within the system, leveraging RLS for adaptability and SVD to reveal low-rank structure. The second stage introduces a weighted nuclear norm regularization criterion function, where adaptive weights derived from th…

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…

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…

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 …

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…

Learning Smooth Populations of Parameters with Trial Heterogeneity
JungHo Lee, Valerio Ba\'cak, Edward H. Kennedy
https://arxiv.org/abs/2507.23140 https://

Learning Smooth Populations of Parameters with Trial Heterogeneity
We consider the classical problem of estimating the mixing distribution of binomial mixtures, but under trial heterogeneity and smoothness. This problem has been studied extensively when the trial parameter is homogeneous, but not under the more general scenario of heterogeneous trials, and only within a low smoothness regime, where the resulting rates are slow. Under the assumption that the density is s-smooth, we derive fast error rates for the kernel density estimator under trial heterogenei…

Fragment size density estimator for shrinkage-induced fracture based on a physics-informed neural network
Shin-ichi Ito
https://arxiv.org/abs/2507.11799 ht…

Fragment size density estimator for shrinkage-induced fracture based on a physics-informed neural network
This paper presents a neural network (NN)-based solver for an integro-differential equation that models shrinkage-induced fragmentation. The proposed method directly maps input parameters to the corresponding probability density function without numerically solving the governing equation, thereby significantly reducing computational costs. Specifically, it enables efficient evaluation of the density function in Monte Carlo simulations while maintaining accuracy comparable to or even exceeding t…

Estimating cluster masses: a comparative study between machine learning and maximum likelihood
Raeed Mundow, Adi Nusser
https://arxiv.org/abs/2507.21876 https://

Estimating cluster masses: a comparative study between machine learning and maximum likelihood
We compare an autoencoder convolutional neural network (AE-CNN) with a conventional maximum-likelihood estimator (MLE) for inferring cluster virial masses, $M_v$, directly from the galaxy distribution around clusters, without identifying members or interlopers. The AE-CNN is trained on mock galaxy catalogues, whereas the MLE assumes that clusters of similar mass share the same phase-space galaxy profile. Conceptually, the MLE returns an unbiased estimate of $\log M_v$ at fixed true mass, wherea…

Consistency and Central Limit Results for the Maximum Likelihood Estimator in the Admixture Model
Carola Sophia Heinzel
https://arxiv.org/abs/2507.19564 https://

Consistency and Central Limit Results for the Maximum Likelihood Estimator in the Admixture Model
In the Admixture Model, the probability of an individual having a certain number of alleles at a specific marker depends on the allele frequencies in $K$ ancestral populations and the fraction of the individual's genome originating from these ancestral populations. This study investigates consistency and central limit results of maximum likelihood estimators (MLEs) for the ancestry and the allele frequencies in the Admixture Model, complimenting previous work by \cite{pfaff2004information, pf…

Tree-based adaptive finite element methods for deformable image registration
Nicol\'as A. Barnafi, Alberto F. Mart{\i}n, Ricardo Ruiz-Baier
https://arxiv.org/abs/2506.15876

Tree-based adaptive finite element methods for deformable image registration
In this work we propose an adaptive Finite Element Method (FEM) formulation for the Deformable Image Registration problem (DIR) together with a residual-based a posteriori error estimator, whose efficiency and reliability are theoretically established. This estimator is used to guide Adaptive Mesh Refinement and coarsening (AMR). The nonlinear Euler-Lagrange equations associated with the minimisation of the relevant functional are solved with a pseudo time-stepping fixed-point scheme which is f…

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 …

Derivative Estimation from Coarse, Irregular, Noisy Samples: An MLE-Spline Approach
Konstantin E. Avrachenkov, Leonid B. Freidovich
https://arxiv.org/abs/2507.22176 https://

Derivative Estimation from Coarse, Irregular, Noisy Samples: An MLE-Spline Approach
We address numerical differentiation under coarse, non-uniform sampling and Gaussian noise. A maximum-likelihood estimator with $L_2$-norm constraint on a higher-order derivative is obtained, yielding spline-based solution. We introduce a non-standard parameterization of quadratic splines and develop recursive online algorithms. Two formulations -- quadratic and zero-order -- offer tradeoff between smoothness and computational speed. Simulations demonstrate superior performance over high-gain o…

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…

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…

Networked pointing system: Bearing-only target localization and pointing control
Shiyao Li, Bo Zhu, Yining Zhou, Jie Ma, Baoqing Yang, Fenghua He
https://arxiv.org/abs/2506.18460 …

Networked pointing system: Bearing-only target localization and pointing control
In the paper, we formulate the target-pointing consensus problem where the headings of agents are required to point at a common target. Only a few agents in the network can measure the bearing information of the target. A two-step solution consisting of a bearing-only estimator for target localization and a control law for target pointing is constructed to address this problem. Compared to the strong assumptions of existing works, we only require two agents not collinear with the target to ensu…

GDNSQ: Gradual Differentiable Noise Scale Quantization for Low-bit Neural Networks
Sergey Salishev, Ian Akhremchik
https://arxiv.org/abs/2508.14004 https://

GDNSQ: Gradual Differentiable Noise Scale Quantization for Low-bit Neural Networks
Quantized neural networks can be viewed as a chain of noisy channels, where rounding in each layer reduces capacity as bit-width shrinks; the floating-point (FP) checkpoint sets the maximum input rate. We track capacity dynamics as the average bit-width decreases and identify resulting quantization bottlenecks by casting fine-tuning as a smooth, constrained optimization problem. Our approach employs a fully differentiable Straight-Through Estimator (STE) with learnable bit-width, noise scale an…

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.

Consistent and Asymptotically Efficient Localization from Bearing-only Measurements
Shenghua Hu, Guangyang Zeng, Wenchao Xue, Haitao Fang, Biqiang Mu
https://arxiv.org/abs/2507.07647

Consistent and Asymptotically Efficient Localization from Bearing-only Measurements
We study the problem of signal source localization using bearing-only measurements. Initially, we present easily verifiable geometric conditions for sensor deployment to ensure the asymptotic identifiability of the model and demonstrate the consistency and asymptotic efficiency of the maximum likelihood (ML) estimator. However, obtaining the ML estimator is challenging due to its association with a non-convex optimization problem. To address this, we propose a two-step estimator that shares the…

Inference in a generalized Bradley-Terry model for paired comparisons with covariates and a growing number of subjects
Ting Yan
https://arxiv.org/abs/2507.22472 https://

Inference in a generalized Bradley-Terry model for paired comparisons with covariates and a growing number of subjects
Motivated by the home-field advantage in sports, we propose a generalized Bradley--Terry model that incorporates covariate information for paired comparisons. It has an $n$-dimensional merit parameter $\bsβ$ and a fixed-dimensional regression coefficient $\bsγ$ for covariates. When the number of subjects $n$ approaches infinity and the number of comparisons between any two subjects is fixed, we show the uniform consistency of the maximum likelihood estimator (MLE) $(\widehat{\bsβ}, \widehat{…

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 …

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…

A further look at Modified ML estimation of the panel AR(1) model with fixed effects and arbitrary initial conditions
Hugo Kruiniger
https://arxiv.org/abs/2508.20753 https://

A further look at Modified ML estimation of the panel AR(1) model with fixed effects and arbitrary initial conditions
In this paper we consider two kinds of generalizations of Lancaster's (Review of Economic Studies, 2002) Modified ML estimator (MMLE) for the panel AR(1) model with fixed effects and arbitrary initial conditions and possibly covariates when the time dimension, T, is fixed. When the autoregressive parameter rho=1, the limiting modified profile log-likelihood function for this model has a stationary point of inflection, and rho is first-order underidentified but second-order identified. We show t…

Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces
G\'abor Lugosi, Marcos Matabuena
https://arxiv.org/abs/2507.15741 h…

Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces
This paper introduces a framework for uncertainty quantification in regression models defined in metric spaces. Leveraging a newly defined notion of homoscedasticity, we develop a conformal prediction algorithm that offers finite-sample coverage guarantees and fast convergence rates of the oracle estimator. In heteroscedastic settings, we forgo these non-asymptotic guarantees to gain statistical efficiency, proposing a local $k$--nearest--neighbor method without conformal calibration that is ad…

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…

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 …

CVIRO: A Consistent and Tightly-Coupled Visual-Inertial-Ranging Odometry on Lie Groups
Yizhi Zhou, Ziwei Kang, Jiawei Xia, Xuan Wang
https://arxiv.org/abs/2508.10867 https://

CVIRO: A Consistent and Tightly-Coupled Visual-Inertial-Ranging Odometry on Lie Groups
Ultra Wideband (UWB) is widely used to mitigate drift in visual-inertial odometry (VIO) systems. Consistency is crucial for ensuring the estimation accuracy of a UWBaided VIO system. An inconsistent estimator can degrade localization performance, where the inconsistency primarily arises from two main factors: (1) the estimator fails to preserve the correct system observability, and (2) UWB anchor positions are assumed to be known, leading to improper neglect of calibration uncertainty. In this …

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…

On the Upper Bounds for the Matrix Spectral Norm
Alexey Naumov, Maxim Rakhuba, Denis Ryapolov, Sergey Samsonov
https://arxiv.org/abs/2506.15660 https://

On the Upper Bounds for the Matrix Spectral Norm
We consider the problem of estimating the spectral norm of a matrix using only matrix-vector products. We propose a new Counterbalance estimator that provides upper bounds on the norm and derive probabilistic guarantees on its underestimation. Compared to standard approaches such as the power method, the proposed estimator produces significantly tighter upper bounds in both synthetic and real-world settings. Our method is especially effective for matrices with fast-decaying spectra, such as tho…

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…

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 …

Generative model for optimal density estimation on unknown manifold
Arthur St\'ephanovitch
https://arxiv.org/abs/2506.19587 https://

Generative model for optimal density estimation on unknown manifold
We propose a generative model that achieves minimax-optimal convergence rates for estimating probability distributions supported on unknown low-dimensional manifolds. Building on Fefferman's solution to the geometric Whitney problem, our estimator is itself supported on a submanifold that matches the regularity of the data's support. This geometric adaptation enables the estimator to be simultaneously minimax-optimal for all \( γ\)-Hölder Integral Probability Metrics (IPMs) with \( γ\geq 1 \…

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 …

Replaced article(s) found for econ.EM. https://arxiv.org/list/econ.EM/new
[1/1]:
- The purpose of an estimator is what it does: Misspecification, estimands, and over-identification
Isaiah Andrews, Jiafeng Chen, Otavio Tecchio

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.

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…

The purpose of an estimator is what it does: Misspecification, estimands, and over-identification
Isaiah Andrew, Jiafeng Chen, Otavio Tecchio
https://arxiv.org/abs/2508.13076 ht…

The purpose of an estimator is what it does: Misspecification, estimands, and over-identification
In over-identified models, misspecification -- the norm rather than exception -- fundamentally changes what estimators estimate. Different estimators imply different estimands rather than different efficiency for the same target. A review of recent applications of generalized method of moments in the American Economic Review suggests widespread acceptance of this fact: There is little formal specification testing and widespread use of estimators that would be inefficient were the model correct,…

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…

Comment on "Average Hazard as Harmonic Mean" by Chiba
Hajime Uno, Lu Tian, Miki Horiguchi, Satoshi Hattori
https://arxiv.org/abs/2507.15985 https…

Comment on "Average Hazard as Harmonic Mean" by Chiba
In a recent article published in Pharmaceutical Statistics, Chiba proposed a reinterpretation of the average hazard as a harmonic mean of the hazard function and questioned the validity of the Kaplan-Meier plug-in estimator when the truncation time does not coincide with an observed event time. In this commentary, we examine the arguments presented and highlight several points that warrant clarification. Through simulation studies, we further show that the plug-in estimator provides reliable es…

Nonparametric Inference for Noise Covariance Kernels in Parabolic SPDEs using Space-Time Infill-Asymptotics
Andreas Petersson, Dennis Schroers
https://arxiv.org/abs/2508.20947 h…

Nonparametric Inference for Noise Covariance Kernels in Parabolic SPDEs using Space-Time Infill-Asymptotics
We develop an asymptotic limit theory for nonparametric estimation of the noise covariance kernel in linear parabolic stochastic partial differential equations (SPDEs) with additive colored noise, using space-time infill asymptotics. The method employs discretized infinite-dimensional realized covariations and requires only mild regularity assumptions on the kernel to ensure consistent estimation and asymptotic normality of the estimator. On this basis, we construct omnibus goodness-of-fit test…

One-dimensional Discrete Models of Maximum Likelihood Degree One
Carlos Am\'endola, Viet Duc Nguyen, Janike Oldekop
https://arxiv.org/abs/2507.18686 https://

One-dimensional Discrete Models of Maximum Likelihood Degree One
We settle a conjecture by Bik and Marigliano stating that the degree of a one-dimensional discrete model with rational maximum likelihood estimator is bounded above by a linear function in the size of its support, therefore showing that there are only finitely many fundamental such models for any given number of states. We study these models from a combinatorial perspective with regard to their existence and enumeration. In particular, sharp models, those whose degree attains the maximal bound,…

Replaced article(s) found for math.ST. https://arxiv.org/list/math.ST/new
[1/1]:
- On Gaussian Approximation for M-Estimator
Masaaki Imaizumi, Taisuke Otsu
http…

Adaptive stable distribution and Hurst exponent by method of moments moving estimator for nonstationary time series
Jarek Duda
https://arxiv.org/abs/2506.05354

Adaptive stable distribution and Hurst exponent by method of moments moving estimator for nonstationary time series
Nonstationarity of real-life time series requires model adaptation. In classical approaches like ARMA-ARCH there is assumed some arbitrarily chosen dependence type. To avoid their bias, we will focus on novel more agnostic approach: moving estimator, which estimates parameters separately for every time $t$: optimizing $F_t=\sum_{τ

Asymptotic Inference for Exchangeable Gibbs Partition
Takuya Koriyama
https://arxiv.org/abs/2506.21527 https://arxiv.org/pdf/2506.215…

Asymptotic Inference for Exchangeable Gibbs Partition
We study the asymptotic properties of parameter estimation and predictive inference under the exchangeable Gibbs partition, characterized by a discount parameter $α\in(0,1)$ and a triangular array $v_{n,k}$ satisfying a backward recursion. Assuming that $v_{n,k}$ admits a mixture representation over the Ewens--Pitman family $(α, θ)$, with $θ$ integrated by an unknown mixing distribution, we show that the (quasi) maximum likelihood estimator $\hatα_n$ (QMLE) for $α$ is asymptotically mixed…

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…

Riesz representers for the rest of us
Nicholas T. Williams, Oliver J. Hines, Kara E. Rudolph
https://arxiv.org/abs/2507.19413 https://arxiv.org/pdf/2507.19…

Riesz representers for the rest of us
The application of semiparametric efficient estimators, particularly those that leverage machine learning, is rapidly expanding within epidemiology and causal inference. Much of the recent methodological literature on these estimators relies heavily on the Riesz representation theorem and Riesz regression. This paper aims to introduce the Riesz representation theorem to an applied audience, explaining why and how Riesz regression is becoming widely used in the semiparametric estimator statistic…

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…

Nonparametric Estimation For Censored Circular Data
Nicolas Conanec
https://arxiv.org/abs/2508.06150 https://arxiv.org/pdf/2508.06150

Nonparametric Estimation For Censored Circular Data
We study the problem of estimating the probability density function of a circular random variable subject to censoring. To this end, we propose a fully computable quotient estimator that combines a projection estimator on linear sieves with a method-of-moments approach. We derive an upper bound for its mean integrated squared error and establish convergence rates when the underlying density lies in a Sobolev class. The practical performance of the estimator is illustrated through simulated exam…