Tootfinder

Nonparametric Vector Quantile Autoregression
Alberto Gonz\'alez-Sanz, Marc Hallin, Yisha Yao
https://arxiv.org/abs/2510.03166 https://arxiv.org/pdf/251…

Nonparametric Vector Quantile Autoregression
Prediction is a key issue in time series analysis. Just as classical mean regression models, classical autoregressive methods, yielding L$^2$ point-predictions, provide rather poor predictive summaries; a much more informative approach is based on quantile (auto)regression, where the whole distribution of future observations conditional on the past is consistently recovered. Since their introduction by Koenker and Xiao in 2006, autoregressive quantile autoregression methods have become a popula…

Comparative Analysis of Parameterized Action Actor-Critic Reinforcement Learning Algorithms for Web Search Match Plan Generation
Ubayd Bapoo, Clement N Nyirenda
https://arxiv.org/abs/2510.03064

Comparative Analysis of Parameterized Action Actor-Critic Reinforcement Learning Algorithms for Web Search Match Plan Generation
This study evaluates the performance of Soft Actor Critic (SAC), Greedy Actor Critic (GAC), and Truncated Quantile Critics (TQC) in high-dimensional decision-making tasks using fully observable environments. The focus is on parametrized action (PA) spaces, eliminating the need for recurrent networks, with benchmarks Platform-v0 and Goal-v0 testing discrete actions linked to continuous action-parameter spaces. Hyperparameter optimization was performed with Microsoft NNI, ensuring reproducibility…

Semiparametric model averaging for high-dimensional quantile regression with nonignorable nonresponse
Wei Xiong, Dianliang Deng, Dehui Wang
https://arxiv.org/abs/2509.00464 http…

Semiparametric model averaging for high-dimensional quantile regression with nonignorable nonresponse
Model averaging has demonstrated superior performance for ensemble forecasting in high-dimensional framework, its extension to incomplete datasets remains a critical but underexplored challenge. Moreover, identifying the parsimonious model through averaging procedure in quantile regression demands urgent methodological innovation. In this paper, we propose a novel model averaging method for high-dimensional quantile regression with nonignorable missingness. The idea is to relax the parametric c…

Sparse Convex Quantile Regression: A Generalized Benders Decomposition Approach
Xiaoyu Luo, Chuanhou Gao
https://arxiv.org/abs/2509.01936 https://arxiv.org…

Sparse Convex Quantile Regression: A Generalized Benders Decomposition Approach
We develop a scalable algorithmic framework for sparse convex quantile regression (SCQR), addressing key computational challenges in the literature. Enhancing the classical CQR model, we introduce L2-norm regularization and an epsilon-insensitive zone to improve generalization and mitigate overfitting - both theoretically justified and empirically validated. Based on this extension, we improve the SCQR model and propose the first Generalized Benders Decomposition (GBD) algorithm tailored to thi…

Neural Optimal Transport Meets Multivariate Conformal Prediction
Vladimir Kondratyev, Alexander Fishkov, Nikita Kotelevskii, Mahmoud Hegazy, Remi Flamary, Maxim Panov, Eric Moulines
https://arxiv.org/abs/2509.25444

Neural Optimal Transport Meets Multivariate Conformal Prediction
We propose a framework for conditional vector quantile regression (CVQR) that combines neural optimal transport with amortized optimization, and apply it to multivariate conformal prediction. Classical quantile regression does not extend naturally to multivariate responses, while existing approaches often ignore the geometry of joint distributions. Our method parametrizes the conditional vector quantile function as the gradient of a convex potential implemented by an input-convex neural network…

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 …

Quantile Advantage Estimation for Entropy-Safe Reasoning
Junkang Wu, Kexin Huang, Jiancan Wu, An Zhang, Xiang Wang, Xiangnan He
https://arxiv.org/abs/2509.22611 https://

Quantile Advantage Estimation for Entropy-Safe Reasoning
Reinforcement Learning with Verifiable Rewards (RLVR) strengthens LLM reasoning, but training often oscillates between {entropy collapse} and {entropy explosion}. We trace both hazards to the mean baseline used in value-free RL (e.g., GRPO and DAPO), which improperly penalizes negative-advantage samples under reward outliers. We propose {Quantile Advantage Estimation} (QAE), replacing the mean with a group-wise K-quantile baseline. QAE induces a response-level, two-regime gate: on hard queries …

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…

Multivariate quantile regression
Antonio F. Galvao, Gabriel Montes-Rojas
https://arxiv.org/abs/2508.15749 https://arxiv.org/pdf/2508.15749

Multivariate quantile regression
This paper introduces a new framework for multivariate quantile regression based on the multivariate distribution function, termed multivariate quantile regression (MQR). In contrast to existing approaches--such as directional quantiles, vector quantile regression, or copula-based methods--MQR defines quantiles through the conditional probability structure of the joint conditional distribution function. The method constructs multivariate quantile curves using sequential univariate quantile regr…

Optimal placement of wind farms via quantile constraint learning
Wenxiu Feng, Antonio Alc\'antara, Carlos Ruiz
https://arxiv.org/abs/2510.01093 https://

Optimal placement of wind farms via quantile constraint learning
Wind farm placement arranges the size and the location of multiple wind farms within a given region. The power output is highly related to the wind speed on spatial and temporal levels, which can be modeled by advanced data-driven approaches. To this end, we use a probabilistic neural network as a surrogate that accounts for the spatiotemporal correlations of wind speed. This neural network uses ReLU activation functions so that it can be reformulated as mixed-integer linear set of constraints …

A Data-Adaptive Factor Model Using Composite Quantile Approach
Seeun Park, Hee-Seok Oh
https://arxiv.org/abs/2510.00558 https://arxiv.org/pdf/2510.00558

A Data-Adaptive Factor Model Using Composite Quantile Approach
This paper proposes a data-adaptive factor model (DAFM), a novel framework for extracting common factors that explain the structures of high-dimensional data. DAFM adopts a composite quantile strategy to adaptively capture the full distributional structure of the data, thereby enhancing estimation accuracy and revealing latent patterns that are invisible to conventional factor models. In this paper, we develop a practical algorithm for estimating DAFM by minimizing an objective function based o…

Differentially Private Learning of Exponential Distributions: Adaptive Algorithms and Tight Bounds
Bar Mahpud, Or Sheffet
https://arxiv.org/abs/2510.00790 https://

Differentially Private Learning of Exponential Distributions: Adaptive Algorithms and Tight Bounds
We study the problem of learning exponential distributions under differential privacy. Given $n$ i.i.d.\ samples from $\mathrm{Exp}(λ)$, the goal is to privately estimate $λ$ so that the learned distribution is close in total variation distance to the truth. We present two complementary pure DP algorithms: one adapts the classical maximum likelihood estimator via clipping and Laplace noise, while the other leverages the fact that the $(1-1/e)$-quantile equals $1/λ$. Each method excels in a d…

Risk level dependent Minimax Quantile lower bounds for Interactive Statistical Decision Making
Raghav Bongole, Amirreza Zamani, Tobias J. Oechtering, Mikael Skoglund
https://arxiv.org/abs/2510.05808

Risk level dependent Minimax Quantile lower bounds for Interactive Statistical Decision Making
Minimax risk and regret focus on expectation, missing rare failures critical in safety-critical bandits and reinforcement learning. Minimax quantiles capture these tails. Three strands of prior work motivate this study: minimax-quantile bounds restricted to non-interactive estimation; unified interactive analyses that focus on expected risk rather than risk level specific quantile bounds; and high-probability bandit bounds that still lack a quantile-specific toolkit for general interactive prot…

Piquant$\varepsilon$: Private Quantile Estimation in the Two-Server Model
Hannah Keller, Jacob Imola, Fabrizio Boninsegna, Rasmus Pagh, Amrita Roy Chowdhury
https://arxiv.org/abs/2509.14035

Piquant$\varepsilon$: Private Quantile Estimation in the Two-Server Model
Quantiles are key in distributed analytics, but computing them over sensitive data risks privacy. Local differential privacy (LDP) offers strong protection but lower accuracy than central DP, which assumes a trusted aggregator. Secure multi-party computation (MPC) can bridge this gap, but generic MPC solutions face scalability challenges due to large domains, complex secure operations, and multi-round interactions. We present Piquant$\varepsilon$, a system for privacy-preserving estimation of…

Federated Learning of Quantile Inference under Local Differential Privacy
Leheng Cai, Qirui Hu, Shuyuan Wu
https://arxiv.org/abs/2509.21800 https://arxiv.o…

Federated Learning of Quantile Inference under Local Differential Privacy
In this paper, we investigate federated learning for quantile inference under local differential privacy (LDP). We propose an estimator based on local stochastic gradient descent (SGD), whose local gradients are perturbed via a randomized mechanism with global parameters, making the procedure tolerant of communication and storage constraints without compromising statistical efficiency. Although the quantile loss and its corresponding gradient do not satisfy standard smoothness conditions typica…

Replaced article(s) found for q-fin.RM. https://arxiv.org/list/q-fin.RM/new
[1/1]:
- Russia-Ukraine conflict and the quantile return connectedness of grain futures in the BRICS and i...
Yan-Hong Yang, Ying-Hui Shao, Wei-Xing Zhou

From Distributional to Quantile Neural Basis Models: the case of Electricity Price Forecasting
Alessandro Brusaferri, Danial Ramin, Andrea Ballarino
https://arxiv.org/abs/2509.14113

From Distributional to Quantile Neural Basis Models: the case of Electricity Price Forecasting
While neural networks are achieving high predictive accuracy in multi-horizon probabilistic forecasting, understanding the underlying mechanisms that lead to feature-conditioned outputs remains a significant challenge for forecasters. In this work, we take a further step toward addressing this critical issue by introducing the Quantile Neural Basis Model, which incorporates the interpretability principles of Quantile Generalized Additive Models into an end-to-end neural network training framewo…

Quantile Function-Based Models for Neuroimaging Classification Using Wasserstein Regression
Jie Li, Gary Green, Jian Zhang
https://arxiv.org/abs/2508.21523 https://

Quantile Function-Based Models for Neuroimaging Classification Using Wasserstein Regression
We propose a novel quantile function-based approach for neuroimaging classification using Wasserstein-Fréchet regression, specifically applied to the detection of mild traumatic brain injury (mTBI) based on the MEG and MRI data. Conventional neuroimaging classification methods for mTBI detection typically extract summary statistics from brain signals across the different epochs, which may result in the loss of important distributional information, such as variance, skewness, kurtosis, etc. Our…

Generalized random forest for extreme quantile regression
Lucien M. Vidagbandji, Alexandre Berred, Cyrille Bertelle, Laurent Amanton
https://arxiv.org/abs/2508.15095 https://

Generalized random forest for extreme quantile regression
Quantile regression is a statistical method which, unlike classical regression, aims to predict the conditional quantiles. Classical quantile regression methods face difficulties, particularly when the quantile under consideration is extreme, due to the limited number of data available in the tail of the distribution, or when the quantile function is complex. We propose an extreme quantile regression method based on extreme value theory and statistical learning to overcome these difficulties. F…

An efficient algorithm for kernel quantile regression
Shengxiang Deng, Xudong Li, Yangjing Zhang
https://arxiv.org/abs/2510.07929 https://arxiv.org/pdf/251…

An efficient algorithm for kernel quantile regression
Kernel Quantile Regression (KQR) extends classical quantile regression to nonlinear settings using kernel methods, offering powerful tools for modeling conditional distributions. However, its application to large-scale datasets is severely limited by the computational burden of the large, dense kernel matrix and the need to efficiently solve large, often ill-conditioned linear systems. Existing state-of-the-art solvers usually struggle with scalability. In this paper, we propose a novel and hig…

Utilitarian or Quantile-Welfare Evaluation of Health Policy?
Charles F. Manski, John Mullahy
https://arxiv.org/abs/2509.05529 https://arxiv.org/pdf/2509.05…

Utilitarian or Quantile-Welfare Evaluation of Health Policy?
This paper considers quantile-welfare evaluation of health policy as an alternative to utilitarian evaluation. Manski (1988) originally proposed and studied maximization of quantile utility as a model of individual decision making under uncertainty, juxtaposing it with maximization of expected utility. That paper's primary motivation was to exploit the fact that maximization of quantile utility requires only an ordinal formalization of utility, not a cardinal one. This paper transfers these ide…

2025 Southeast Asia Eleven Nations Influence Index Report
Wei Meng
https://arxiv.org/abs/2509.19953 https://arxiv.org/pdf/2509.19953

2025 Southeast Asia Eleven Nations Influence Index Report
This study constructs a fully data-driven and reproducible Southeast Asia Influence Index (SAII v3) to reduce bias from expert scoring and subjective weighting while mapping hierarchical power structures across the eleven ASEAN nations. We aggregate authoritative open-source indicators across four dimensions (economic, military, diplomatic, socio-technological) and apply a three-tiered standardization chain quantile-Box-Cox-min-max to mitigate outliers and skewness. Weights are obtained through…

Quantile-based Fractional Generalized Cumulative Past Entropy
Poulami Paul, Chanchal Kundu
https://arxiv.org/abs/2509.09182 https://arxiv.org/pdf/2509.0918…

Quantile-based Fractional Generalized Cumulative Past Entropy
Uncertainty in past lifetime distributions and the timing of inactivity in systems and their components can be effectively measured using the fractional generalized cumulative past entropy (FGCPE) and its dynamic extension (DFGCPE), introduced by Di Crescenzo et al. (2021). Building on this framework, we propose a quantile-based variant, the quantile fractional generalized cumulative past entropy (QFGCPE), along with its dynamic time-dependent counterpart (DQFGCPE). Closed-form expressions of t…

Localized Uncertainty Quantification in Random Forests via Proximities
Jake S. Rhodes, Scott D. Brown, J. Riley Wilkinson
https://arxiv.org/abs/2509.22928 https://

Localized Uncertainty Quantification in Random Forests via Proximities
In machine learning, uncertainty quantification helps assess the reliability of model predictions, which is important in high-stakes scenarios. Traditional approaches often emphasize predictive accuracy, but there is a growing focus on incorporating uncertainty measures. This paper addresses localized uncertainty quantification in random forests. While current methods often rely on quantile regression or Monte Carlo techniques, we propose a new approach using naturally occurring test sets and s…

Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty
Lingyou Pang, Lei Huang, Jianyu Lin, Tianyu Wang, Akira Horiguchi, Alexander Aue, Carey E. Priebe
https://arxiv.org/abs/2509.23002

Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty
Deploying black-box LLMs requires managing uncertainty in the absence of token-level probability or true labels. We propose introducing an unsupervised conformal inference framework for generation, which integrates: generative models, incorporating: (i) an LLM-compatible atypical score derived from response-embedding Gram matrix, (ii) UCP combined with a bootstrapping variant (BB-UCP) that aggregates residuals to refine quantile precision while maintaining distribution-free, finite-sample cover…

On function-on-function linear quantile regression
Muge Mutis, Ufuk Beyaztas, Filiz Karaman, Han Lin Shang
https://arxiv.org/abs/2510.10792 https://arxiv.o…

On function-on-function linear quantile regression
We present two innovative functional partial quantile regression algorithms designed to accurately and efficiently estimate the regression coefficient function within the function-on-function linear quantile regression model. Our algorithms utilize functional partial quantile regression decomposition to effectively project the infinite-dimensional response and predictor variables onto a finite-dimensional space. Within this framework, the partial quantile regression components are approximated …

Monotone tail functions: definitions, properties, and application to risk-reducing strategies
Hamza Hanbali, Daniel Linders
https://arxiv.org/abs/2508.12608 https://

Monotone tail functions: definitions, properties, and application to risk-reducing strategies
This paper studies properties of functions having monotone tails. We extend Theorem 1 of Dhaene et al. (2002a) and show how the tail quantiles of a random variable transformed with a monotone tail function can be expressed as the transformed tail quantiles of the original random variable. The main result is intuitive, in that Dhaene et al. (2002a) properties still hold, but only for certain quantile values. However, the proof presents some complications that arise especially when the function i…

High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inference
Ziliang Shen, Caixing Wang, Shaoli Wang, Yibo Yan
https://arxiv.org/abs/2508.05212

High-Dimensional Differentially Private Quantile Regression: Distributed Estimation and Statistical Inference
With the development of big data and machine learning, privacy concerns have become increasingly critical, especially when handling heterogeneous datasets containing sensitive personal information. Differential privacy provides a rigorous framework for safeguarding individual privacy while enabling meaningful statistical analysis. In this paper, we propose a differentially private quantile regression method for high-dimensional data in a distributed setting. Quantile regression is a powerful an…

Multinomial probit model based on joint quantile regression
Masaaki Okabe, Koki Matsuoka, Jun Tsuchida, Hiroshi Yadohisa
https://arxiv.org/abs/2508.13556 https://

Multinomial probit model based on joint quantile regression
The multinomial probit model is a typical statistical model for multiple-choice data applied in many research areas. When we are interested in some quantiles of relative utilities for understanding the distribution of these utilities, the multinomial probit model is unsuitable because we only interpret the expectation of relative utilities based on it. We thus propose quantile regression analysis methods for multinomial choice data based on joint quantile regression and multinomial probit model…

Replaced article(s) found for econ.EM. https://arxiv.org/list/econ.EM/new
[1/1]:
- Joint Quantile Shrinkage: A State-Space Approach toward Non-Crossing Bayesian Quantile Models
David Kohns, Tibor Szendrei

Probabilistic Pretraining for Neural Regression
Boris N. Oreshkin, Shiv Tavker, Dmitry Efimov
https://arxiv.org/abs/2508.16355 https://arxiv.org/pdf/2508.1…

Probabilistic Pretraining for Neural Regression
Transfer learning for probabilistic regression remains underexplored. This work closes this gap by introducing NIAQUE, Neural Interpretable Any-Quantile Estimation, a new model designed for transfer learning in probabilistic regression through permutation invariance. We demonstrate that pre-training NIAQUE directly on diverse downstream regression datasets and fine-tuning it on a specific target dataset enhances performance on individual regression tasks, showcasing the positive impact of proba…

Strong uniform consistency of nonparametric estimation for quantile-based entropy function under length-biased sampling
Vaishnavi Pavithradas, Rajesh G
https://arxiv.org/abs/2509.15734

Strong uniform consistency of nonparametric estimation for quantile-based entropy function under length-biased sampling
For studies in reliability, biometry, and survival analysis, the length-biased distribution is often well-suited for certain natural sampling plans. In this paper, we study the strong uniform consistency of two nonparametric estimators for the quantile-based Shannon entropy in the context of length-biased data. A simulation study is conducted to examine the behavior of the estimators in finite samples, followed by a comparative analysis with existing estimators. Furthermore, the usefulness of t…

Mean and quantile regression in the copula setting: properties, sharp bounds and a note on estimation
Henrik Kaiser, Wolfgang Trutschnig
https://arxiv.org/abs/2510.03804 https:/…

Mean and quantile regression in the copula setting: properties, sharp bounds and a note on estimation
Driven by the interest on how uniformity of marginal distributions propa\-gates to properties of regression functions, in this contribution we tackle the following questions: Given a $(d-1)$-dimensional random vector $\textbf{X}$ and a random variable $Y$ such that all univariate marginals of $(\textbf{X},Y)$ are uniformly distributed on $[0,1]$, how large can the average absolute deviation of the mean and the quantile regression function of $Y$ given $\textbf{X}$ from the value $\frac{1}{2}$ b…

Combining a Large Pool of Forecasts of Value-at-Risk and Expected Shortfall
James W. Taylor, Chao Wang
https://arxiv.org/abs/2508.16919 https://arxiv.org/p…

Combining a Large Pool of Forecasts of Value-at-Risk and Expected Shortfall
Value-at-risk (VaR) and expected shortfall (ES) have become widely used measures of risk for daily portfolio returns. As a result, many methods now exist for forecasting the VaR and ES. These include GARCH-based modelling, approaches involving quantile-based autoregressive models, and methods incorporating measures of realised volatility. When multiple forecasting methods are available, an alternative to method selection is forecast combination. In this paper, we consider the combination of a l…

Replaced article(s) found for q-fin.PR. https://arxiv.org/list/q-fin.PR/new
[1/1]:
- Beyond Volatility: Common Factors in Idiosyncratic Quantile Risks
Jozef Barunik, Matej Nevrla

Replaced article(s) found for q-fin.GN. https://arxiv.org/list/q-fin.GN/new
[1/1]:
- Beyond Volatility: Common Factors in Idiosyncratic Quantile Risks
Jozef Barunik, Matej Nevrla

QDeepGR4J: Quantile-based ensemble of deep learning and GR4J hybrid rainfall-runoff models for extreme flow prediction with uncertainty quantification
Arpit Kapoor, Rohitash Chandra
https://arxiv.org/abs/2510.05453

QDeepGR4J: Quantile-based ensemble of deep learning and GR4J hybrid rainfall-runoff models for extreme flow prediction with uncertainty quantification
Conceptual rainfall-runoff models aid hydrologists and climate scientists in modelling streamflow to inform water management practices. Recent advances in deep learning have unravelled the potential for combining hydrological models with deep learning models for better interpretability and improved predictive performance. In our previous work, we introduced DeepGR4J, which enhanced the GR4J conceptual rainfall-runoff model using a deep learning model to serve as a surrogate for the routing comp…

Quantile-Scaled Bayesian Optimization Using Rank-Only Feedback
Tunde Fahd Egunjobi
https://arxiv.org/abs/2510.03277 https://arxiv.org/pdf/2510.03277…

Quantile-Scaled Bayesian Optimization Using Rank-Only Feedback
Bayesian Optimization (BO) is widely used for optimizing expensive black-box functions, particularly in hyperparameter tuning. However, standard BO assumes access to precise objective values, which may be unavailable, noisy, or unreliable in real-world settings where only relative or rank-based feedback can be obtained. In this study, we propose Quantile-Scaled Bayesian Optimization (QS-BO), a principled rank-based optimization framework. QS-BO converts ranks into heteroscedastic Gaussian targe…

Unit-Modified Weibull Distribution and Quantile Regression Model
Jo\~ao In\'acio Scrimini, Cleber Bisognin, Renata Rojas Guerra, F\'abio M. Bayer
https://arxiv.org/abs/2508.17359

Unit-Modified Weibull Distribution and Quantile Regression Model
The Sustainable Development Goals (SDGs) of the United Nations consist of 17 general objectives, subdivided into 169 targets to be achieved by 2030. Several SDG indices and indicators require continuous analysis and evaluation, and most of these indices are supported in the unit interval (0,1). To incorporate the flexibility of the modified Weibull (MW) distribution in doubly constrained datasets, the first objective of this work is to propose a new unit probability distribution based on the MW…

Heterogeneous Quantile Treatment Effect Estimation for Longitudinal Data with High-Dimensional Confounding
Zhixin Qiu, Huichen Zhu, Wenjie Wang, Yanlin Tang
https://arxiv.org/abs/2508.16326

Heterogeneous Quantile Treatment Effect Estimation for Longitudinal Data with High-Dimensional Confounding
Causal inference plays a fundamental role in various real-world applications. However, in the motivating non-small cell lung cancer (NSCLC) study, it is challenging to estimate the treatment effect of chemotherapy on circulating tumor DNA (ctDNA). First, the heterogeneous treatment effects vary across patient subgroups defined by baseline characteristics. Second, there exists a broad set of demographic, clinical and molecular variables act as potential confounders. Third, ctDNA trajectories ove…

Replaced article(s) found for stat.CO. https://arxiv.org/list/stat.CO/new
[1/1]:
- Quantile Importance Sampling
Jyotishka Datta, Nicholas G. Polson
https://

Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods
Martin J\'anos Mayer, \'Agnes Baran, Sebastian Lerch, Nina Horat, Dazhi Yang, S\'andor Baran
https://arxiv.org/abs/2508.15508

Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods
Accurate and reliable forecasting of photovoltaic (PV) power generation is crucial for grid operations, electricity markets, and energy planning, as solar systems now contribute a significant share of the electricity supply in many countries. PV power forecasts are often generated by converting forecasts of relevant weather variables to power predictions via a model chain. The use of ensemble simulations from numerical weather prediction models results in probabilistic PV forecasts in the form …

General CoVaR Based on Entropy Pooling
Yuhong Xu, Xinyao Zhao
https://arxiv.org/abs/2509.21904 https://arxiv.org/pdf/2509.21904

General CoVaR Based on Entropy Pooling
We propose a general CoVaR framework that extends the traditional CoVaR by incorporating diverse expert views and information, such as asset moment characteristics, quantile insights, and perspectives on the relative loss distribution between two assets. To integrate these expert views effectively while minimizing deviations from the prior distribution, we employ the entropy pooling method to derive the posterior distribution, which in turn enables us to compute the general CoVaR. Assuming biva…

Quantile estimation of CO2 marginal abatement cost across emission-generating technologies
Haleh Delnava, Sheng Dai
https://arxiv.org/abs/2508.11912 https://

Quantile estimation of CO2 marginal abatement cost across emission-generating technologies
Marginal abatement cost (MAC) is a critical metric for designing efficient and cost-effective mitigation policies. However, existing MAC estimates are typically derived under different assumptions about emission-generating technologies, yet few studies have systematically compared these technologies. Moreover, conventional estimators often exhibit biases arising from limited abatement options, production inefficiencies, and data noise. To address these limitations, this paper analyzes the abate…

An efficient hybrid approach of quantile and expectile regression
Abdellah Atanane, Abdallah Mkhadri, Karim Oualkacha
https://arxiv.org/abs/2510.05268 https://

An efficient hybrid approach of quantile and expectile regression
Quantiles and expectiles are determined by different loss functions: asymmetric least absolute deviation for quantiles and asymmetric squared loss for expectiles. This distinction ensures that quantile regression methods are robust to outliers but somewhat less effective than expectile regression, especially for normally distributed data. However, expectile regression is vulnerable to lack of robustness, especially for heavy-tailed distributions. To address this trade-off between robustness and…

Non-Asymptotic Analysis of Efficiency in Conformalized Regression
Yunzhen Yao, Lie He, Michael Gastpar
https://arxiv.org/abs/2510.07093 https://arxiv.org/p…

Non-Asymptotic Analysis of Efficiency in Conformalized Regression
Conformal prediction provides prediction sets with coverage guarantees. The informativeness of conformal prediction depends on its efficiency, typically quantified by the expected size of the prediction set. Prior work on the efficiency of conformalized regression commonly treats the miscoverage level $α$ as a fixed constant. In this work, we establish non-asymptotic bounds on the deviation of the prediction set length from the oracle interval length for conformalized quantile and median regre…

Adapting Noise to Data: Generative Flows from 1D Processes
Jannis Chemseddine, Gregor Kornhardt, Richard Duong, Gabriele Steidl
https://arxiv.org/abs/2510.12636 https://

Adapting Noise to Data: Generative Flows from 1D Processes
We introduce a general framework for constructing generative models using one-dimensional noising processes. Beyond diffusion processes, we outline examples that demonstrate the flexibility of our approach. Motivated by this, we propose a novel framework in which the 1D processes themselves are learnable, achieved by parameterizing the noise distribution through quantile functions that adapt to the data. Our construction integrates seamlessly with standard objectives, including Flow Matching an…

Crosslisted article(s) found for math.ST. https://arxiv.org/list/math.ST/new
[1/1]:
- Quantile-Scaled Bayesian Optimization Using Rank-Only Feedback
Tunde Fahd Egunjobi

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- fastkqr: A Fast Algorithm for Kernel Quantile Regression
Qian Tang, Yuwen Gu, Boxiang Wang

Forecasting Extreme Day and Night Heat in Paris
Richard Berk
https://arxiv.org/abs/2508.12886 https://arxiv.org/pdf/2508.12886

Forecasting Extreme Day and Night Heat in Paris
As a demonstration of concept, quantile gradient boosting is used to forecast diurnal and nocturnal Q(.90) air temperatures for Paris, France during late the spring and summer months of 2020. The data are provided by the Paris-Montsouris weather station. Q(.90) values are estimated because the 90th percentile requires that the temperatures be relatively rare and extreme. Predictors include seven routinely collected indicators of weather conditions, lagged by 14 days; the temperature forecasts a…

Record-based transmuted log-logistic distribution: Properties, simulation, and applications to petroleum rock and reactor pump data
Caner Tan{\i}\c{s}
https://arxiv.org/abs/2509.09757

Record-based transmuted log-logistic distribution: Properties, simulation, and applications to petroleum rock and reactor pump data
This study aims to introduce a new lifetime distribution, called the record-based transformed log-logistic distribution, to the literature. We obtain this distribution using a record-based transformation map based on the distributions of upper record values. We explore some mathematical properties of the suggested distribution, namely the quantile function, hazard function, moments, order statistics, and stochastic ordering. We discuss the point estimation via seven different methods such as ma…

Computational Study of New Record-Based Transmuted Chen Distribution and Its Applications to the Failure Time and Iron Sheet Data
Caner Tan{\i}\c{s}
https://arxiv.org/abs/2509.09756

Computational Study of New Record-Based Transmuted Chen Distribution and Its Applications to the Failure Time and Iron Sheet Data
This study is considered to introduce a novel distribution as an alternative to Chen distribution via the record-based transmutation method. This technique is based on the distributions of first two upper record values. Thus, we suggest a new special case based on Chen distribution in the family of record based transmuted distributions. We explore various distributional properties of the proposed model namely, quantile function, hazard function, median, moments, and stochastic ordering. Our dis…

Replaced article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
- Multiscale Quantile Regression with Local Error Control
Zhi Liu, Housen Li
ht…