Statistical Methods and Inference

Neueste Paper

• Kernel conditional tests from learning-theoretic bounds

  Pierre-Fran\c{c}ois Massiani, Christian Fiedler, Lukas Haverbeck, Friedrich Solowjow, Sebastian Trimpe · 3. November 2025

  We propose a framework for hypothesis testing on conditional probability distributions, which we then use to construct statistical tests of functionals of conditional distributions. These tests identify the inputs where the functionals differ with high probability, and include tests of conditional m…

• Minimax-Optimal Two-Sample Test with Sliced Wasserstein

  Binh Thuan Tran, Nicolas Schreuder · 3. November 2025

  We study the problem of nonparametric two-sample testing using the sliced Wasserstein (SW) distance. While prior theoretical and empirical work indicates that the SW distance offers a promising balance between strong statistical guarantees and computational efficiency, its theoretical foundations fo…

• $L_1$-norm Regularized Indefinite Kernel Logistic Regression

  Shaoxin Wang, Hanjing Yao · 31. Oktober 2025

  Kernel logistic regression (KLR) is a powerful classification method widely applied across diverse domains. In many real-world scenarios, indefinite kernels capture more domain-specific structural information than positive definite kernels. This paper proposes a novel $L_1$-norm regularized indefini…

• Using latent representations to link disjoint longitudinal data for mixed-effects regression

  Clemens Sch\"achter, Maren Hackenberg, Michelle Pfaffenlehner, F\'elix B. Tambe-Ndonfack, Thorsten Schmidt, Astrid Pechmann, Janbernd Kirschner, Jan Hasenauser, Harald Binder · 30. Oktober 2025

  Many rare diseases offer limited established treatment options, leading patients to switch therapies when new medications emerge. To analyze the impact of such treatment switches within the low sample size limitations of rare disease trials, it is important to use all available data sources. This, h…

• Nearest Neighbor Matching as Least Squares Density Ratio Estimation and Riesz Regression

  Masahiro Kato · 29. Oktober 2025

  This study proves that Nearest Neighbor (NN) matching can be interpreted as an instance of Riesz regression for automatic debiased machine learning. Lin et al. (2023) shows that NN matching is an instance of density-ratio estimation with their new density-ratio estimator. Chernozhukov et al. (2024) …

• Copula-Stein Discrepancy: A Generator-Based Stein Operator for Archimedean Dependence

  Agnideep Aich, Ashit Baran Aich · 29. Oktober 2025

  Kernel Stein discrepancies (KSDs) have become a principal tool for goodness-of-fit testing, but standard KSDs are often insensitive to higher-order dependency structures, such as tail dependence, which are critical in many scientific and financial domains. We address this gap by introducing the Copu…

• Testing-driven Variable Selection in Bayesian Modal Regression

  Jiasong Duan, Hongmei Zhang, Xianzheng Huang · 29. Oktober 2025

  We propose a Bayesian variable selection method in the framework of modal regression for heavy-tailed responses. An efficient expectation-maximization algorithm is employed to expedite parameter estimation. A test statistic is constructed to exploit the shape of the model error distribution to effec…

• Locally Adaptive Conformal Inference for Operator Models

  Trevor Harris, Yan Liu · 28. Oktober 2025

  Operator models are regression algorithms between Banach spaces of functions. They have become an increasingly critical tool for spatiotemporal forecasting and physics emulation, especially in high-stakes scenarios where robust, calibrated uncertainty quantification is required. We introduce Local S…

• Deep Copula Classifier: Theory, Consistency, and Empirical Evaluation

  Agnideep Aich, Ashit Baran Aich · 28. Oktober 2025

  We present the Deep Copula Classifier (DCC), a class-conditional generative model that separates marginal estimation from dependence modeling using neural copula densities. DCC is interpretable, Bayes-consistent, and achieves excess-risk $O(n^{-r/(2r+d)})$ for $r$-smooth copulas. In a controlled two…

• Dimension-free Score Matching and Time Bootstrapping for Diffusion Models

  Syamantak Kumar, Dheeraj Nagaraj, Purnamrita Sarkar · 28. Oktober 2025

  Diffusion models generate samples by estimating the score function of the target distribution at various noise levels. The model is trained using samples drawn from the target distribution by progressively adding noise. Previous sample complexity bounds have polynomial dependence on the dimension $d…

• Direct Debiased Machine Learning via Bregman Divergence Minimization

  Masahiro Kato · 28. Oktober 2025

  We develop a direct debiased machine learning framework comprising Neyman targeted estimation and generalized Riesz regression. Our framework unifies Riesz regression for automatic debiased machine learning, covariate balancing, targeted maximum likelihood estimation (TMLE), and density-ratio estima…

• Confidence Sets for Multidimensional Scaling

  Siddharth Vishwanath, Ery Arias-Castro · 28. Oktober 2025

  We develop a formal statistical framework for classical multidimensional scaling (CMDS) applied to noisy dissimilarity data. We establish distributional convergence results for the embeddings produced by CMDS for various noise models, which enable the construction of \emph{bona~fide} uniform confide…

• Convergence and Generalization of Anti-Regularization for Parametric Models

  Dongseok Kim, Wonjun Jeong, Gisung Oh · 27. Oktober 2025