Statistical Methods and Inference

Derniers papiers

• Beyond Marginal Validity: Finite-Sample Guarantees for Localized Conformal Prediction

  Anton Conrad, Rustam Isaev, Denis Belomestny, Eric Moulines, Sergey Samsonov · 7 août 2026

  Conformal prediction endows arbitrary black-box predictors with finite-sample, distribution-free marginal coverage, yet marginal validity can hide severe covariate-specific miscalibration, while exact distribution-free conditional coverage is finite-sample unattainable. Randomly localized conformal …

• Nonparametric Goodness-of-fit Testing under Covariate Shift

  Zhen Hou, Dong Xia · 6 août 2026

  This paper develops procedures for nonparametric goodness-of-fit testing under covariate shift, where labelled data are drawn from a source population but goodness-of-fit is evaluated for a target population. The distribution mismatch is quantified by either a bounded moment condition or a sub-expon…

• Double Descent in Gradient Boosting Decision Trees via Split-Candidate Scaling

  Ryuichi Kanoh · 5 août 2026

  Double descent is commonly studied by scaling an explicit capacity parameter, such as neural-network width. For gradient boosting decision trees (GBDTs), however, an analogous single-axis capacity parameter has not been established. We propose the number of split candidates as an operational capacit…

• A Simple Approximation to the Distribution of the Ridge Regression Estimator

  Jos\'e Luis Montiel Olea, Ryan Strong, Amilcar Velez, Zhuoheng Xu, Haomin Yu · 4 août 2026

  We present a simple Gaussian approximation to the finite-sample distribution of the classical ridge regression estimator. Our approximation captures the fact that, in finite samples, the ridge regression estimator trades off bias and variance to reduce estimation and prediction error. Our approximat…

• Active Regression for Single-Index Models with Unknown Link Functions

  Chansophea Wathanak In, Yi Li, Wai Ming Tai, Xuan Wu · 4 août 2026

  This paper studies active regression for single-index models under general $\ell_p$-loss with an unknown $1$-Lipschitz link function $f$, formulated as $\min_{f,x} \|f(Ax)-b\|_p^p$ with full access to $A$ but coordinate-query access to $b$. Prior work established upper bounds for known link function…

• Finite-Probe Total-Variation Certificates for Finite-Basis Drifting Models

  Sam Andersson, Ricky Mol\'en · 4 août 2026

  Drifting objectives compare a target and model distribution through a vector field observed noisily at finitely many locations. We ask what distributional conclusion such a frozen measurement system warrants. For integrable antisymmetric interactions and absolutely continuous laws in a declared fini…

• Beyond Modern Asymptotics for Log-Likelihood Ratios in Logistic Regression

  Hugo Chardon, Reese Pathak, Nikita Zhivotovskiy · 4 août 2026

  We characterize the finite sample behavior of the log-likelihood ratio statistic in binary logistic regression, uniformly over both the design and the target parameter. For $n\geq d\geq 3$, we determine, up to universal constants, its worst case $(1-\delta)$ quantile over all fixed collections of de…

• A reproducible and extensible framework for benchmarking competing risks survival models

  Bego\~na B. Sierra, Colin McLean, Peter S. Hall, Sarah Friedrich-Welz, Catalina A. Vallejos · 4 août 2026

  A wide range of statistical and machine learning methods have been proposed for survival analysis with competing risks, where the occurrence of one event (i.e., cancer death) precludes the occurrence of other events (i.e., cardiovascular disease death). Despite these methodological advances, their s…

• Who Wins Where? Conformal Model Comparison for Local Superiority

  Yi Zhou, Baishi Li, Xuan Yao, Ke-Wei Huang · 3 août 2026

  Standard model comparison is global, aggregating losses across the covariate space to declare a single winner. This can obscure heterogeneous performance, where different models are preferable in different regions. We introduce conformalized local model comparison, a split-sample framework for const…

• Retrospective Orthogonal Design: Response-Surface Reconstruction from Observational Data

  Lawrence Fulton, Christopher Fulton, Arvind Sharma, Aleksandar Tomic · 30 juillet 2026

  Regression estimates from observational data can depend on specification under multicollinearity, while sequential sums of squares (SS) depend on term order. We introduce Retrospective Orthogonal Design (ROD), which reconstructs conditional mean surfaces on a probability-balanced lattice. ROD preser…

• When Kernel Ridge Regression Meets the H\"older-Zygmund Class: Minimax Optimality and Failure of Properness

  Yuxuan Hou · 30 juillet 2026

  We study kernel ridge regression for nonparametric regression over the H\"older-Zygmund class. Using an RKHS equivalent to a Sobolev space of smoothness s+d/2, we prove that misspecified KRR attains the minimax L2 rate n^{-2s/(2s+d)}. We also show that properness fails in the H\"older-Zygmund norm: …

• Breaking the Curse with BAND: Nonparametric Distribution Estimation in High Dimensions

  Shuo-Chieh Huang, Chien-Ming Chi, Jau-er Chen · 30 juillet 2026

  Minimax-optimal rates for multivariate distribution estimation are known to suffer from the curse of dimensionality. We propose a sparse Bayesian network approach in which each conditional probability is estimated using sparsity-aware conditional mean methods. The resulting estimator, \textit{BAyesi…

• Distributional Split Criteria for Random Forests: Extensions, Shrinkage, and the Robustness of Mean Splitting

  Silas Koemen · 28 juillet 2026

  Distributional random forests replace mean-based CART splitting with criteria that compare the full conditional response distribution in candidate children. We implement and systematically study a family of such criteria inside a single honest-forest implementation: isotropic random-Fourier-feature …

• Automatic knot selection in smooth additive models

  Nicol\'as Carrizosa, Vanesa Guerrero, Mar\'ia Durb\'an · 24 juillet 2026

  B-spline regression constitutes a widely used framework for nonparametric modeling. The performance of this methodology depends on specifying the number and placement of changepoints, known as knots, prior to the estimation process. Such knot sequence determines the dimension of the B-spline basis u…

• Adaptive deep nonparametric regression from dependent data under covariate shift

  William Kengne, Ehud Mossa Ockegna · 23 juillet 2026

  Covariate shift often occurs because, in many real applications, the source and the target observations may be generated from different distributions. In this case, the standard metric under the source distribution is not appropriate. This paper considers deep neural network estimators for nonparame…

• Isotonic Conformal Prediction

  Daniel Bensimon, Sean Xiang Yu, Eric D. Kolaczyk, Archer Y. Yang · 21 juillet 2026

  A point prediction that is well calibrated on average can still be systematically biased conditional on its own value, undermining its use in downstream decision-making. We consider two objectives for reliable uncertainty quantification: self-calibration, requiring a point prediction to be unbiased …

• Pitfalls of Administrative Censoring in Survival Models with Time-Indexed Inputs

  Yanqi Xu, Hui Dai, Carlos Fernandez-Granda, Krzysztof J. Geras, Yiqiu Shen · 14 juillet 2026

  Survival models can model time-to-event outcomes using partially observed data. They are widely used in clinical prediction, including cancer risk, disease progression, treatment response, and mortality. Recent models often rely on rich inputs collected at a specific clinical encounter, such as medi…

• Sharp Concentration Bounds for Bundle-Valued Statistics on Manifolds

  Swagatam Das, Vaclav Snasel · 14 juillet 2026

  Many geometric statistics and manifold learning pipelines routinely produce observations -- such as tangent vectors or local frames -- whose natural home is a varying family of fibers attached to different points of a base manifold, rather than a single shared vector space. Forming empirical average…

• The Regularization Parameter: Sparse Precision Matrix Estimation

  Aryan Eftekhari, Daniel Sergio Vega, Ernst-Jan Camiel Wit, Olaf Schenk · 10 juillet 2026

  Sparse precision matrix estimation provides an interpretable and computationally efficient framework for modeling conditional dependencies in high-dimensional, low-sample-size data. A recurring challenge is appropriately selecting the regularization parameter that controls estimator sparsity and str…

• Approximate full conformal prediction in an RKHS

  Davidson Lova Razafindrakoto, Alain Celisse, J\'er\^ome Lacaille · 9 juillet 2026

  Full conformal prediction is a framework that implicitly formulates distribution-free confidence prediction regions for a wide range of estimators. However, a classical limitation of the full conformal framework is the computation of the confidence prediction regions, which is usually impossible sin…

• K-ABENA: K-Adaptive Backpropagation with Error-based N-exclusion Algorithm : (Compensated Loss-Based Sample Exclusion with Unbiased Gradient Estimation)

  Jean-Francois Bonbhel · 8 juillet 2026

  We present K-ABENA (K-Adaptive Backpropagation with Error-based N-exclusion Algorithm), a selective gradient computation framework that reduces per-iteration training cost by excluding a fraction of low-loss ("minor") observations from the backward pass. Its canonical form (v3) combines a defensive-…

• Distribution-free Deviation Bounds and The Role of Domain Knowledge in Learning via Model Selection with Cross-validation Risk Estimation

  Diego Marcondes, Cl\'audia Peixoto · 7 juillet 2026

  Cross-validation is one of the most widely used tools for risk estimation and model selection in statistics and machine learning, yet its theoretical properties when embedded in a learning procedure remain insufficiently understood. This paper develops a general, distribution-free framework for lear…

• Nonparametric Control Koopman Operators

  Petar Bevanda, Bas Driessen, Lucian Cristian Iacob, Stefan Sosnowski, Roland T\'oth, Sandra Hirche · 7 juillet 2026

  This paper presents a novel Koopman composition operator representation framework for control systems in reproducing kernel Hilbert spaces (RKHSs) that is free of explicit dictionary or input parametrizations. By establishing fundamental equivalences between different model representations, we are a…

• On Pairwise Quantile Regression -- Statistical Guarantees and Applications

  Romain Th\'er\'ezien, Stephan Cl\'emen\c{c}on, Fantin Girard, Hamza El-Abdouni · 7 juillet 2026

  Quantile regression provides a powerful tool for summarizing the conditional distribution of a real valued random variable (r.v.) of interest $Y$ as a function of covariates $Z$ in cases where it shows a large dispersion with high probability, going beyond the situation where standard least square r…

• Aggregation with Exponential Weights is Optimal in Expectation

  Mikael M{\o}ller H{\o}gsgaard, Patrick Rebeschini, Tobias Wegel · 3 juillet 2026

  The aggregation with exponential weights (AEW) estimator is not fully understood in the basic setting of model selection aggregation with squared loss. In particular, whether it is minimax-rate optimal in expectation for large enough fixed temperatures and under random design has been an open proble…