arXiv Statistics @arxiv_stats@qoto.org

One Operator for Many Densities: Amortized Approximation of Conditioning by Neural Operators https://arxiv.org/abs/2605.06873 #stat.ML #math.NA #cs.LG #cs.NA

Kernel Selection is Model Selection: A Unified Complexity-Penalized Approach for MMD Two-Sample Tests https://arxiv.org/abs/2605.06883 #stat.ML #cs.LG

Locally Near Optimal Piecewise Linear Regression in High Dimensions via Difference of Max-Affine Functions https://arxiv.org/abs/2605.06959 #stat.ML #math.ST #stat.TH #cs.LG

Design-Based Variance Estimation for Modern Heterogeneity-Robust Difference-in-Differences Estimators https://arxiv.org/abs/2605.04124 #stat.ME #econ.EM

Heterogeneous Ordinal Structure Learning with Bayesian Nonparametric Complexity Discovery https://arxiv.org/abs/2605.04191 #stat.ML #cs.CY #cs.LG

Optimal Semiparametric Dynamic Pricing with Feature Diversity https://arxiv.org/abs/2605.04207 #q-fin.EC #stat.ME #econ.GN

A multivariate Birnbaum-Saunders autoregressive moving average model with application to air pollution concentration data https://arxiv.org/abs/2605.04211 #stat.ME

BOIN Designs for Dose Escalation With Selected Dose Combinations in Oncology Phase I Trials https://arxiv.org/abs/2605.04212 #stat.ME #stat.AP

Classification-Powered Conformal Inference for Zero-inflated Outcomes https://arxiv.org/abs/2605.04219 #stat.ME

Entropic Riemannian Neural Optimal Transport https://arxiv.org/abs/2605.04255 #stat.ML #stat.ME #cs.LG

Adapt or Forget: Provable Tradeoffs Between Adam and SGD in Nonstationary Optimization https://arxiv.org/abs/2605.04269 #stat.ML #cs.LG

Tweedie-based nonparametric estimation for semicontinuous mixed densities https://arxiv.org/abs/2605.03044 #stat.ME #math.ST #stat.AP #stat.TH

Robust confidence intervals for generalized linear models https://arxiv.org/abs/2605.03060 #stat.ME

Dynamic Vine Copulas: Detecting and Quantifying Time-Varying Higher-Order Interactions https://arxiv.org/abs/2605.03061 #q-bio.QM #stat.ML #stat.ME #cs.LG

Bayesian inference with sources of uncertainty: from confidence modelling to sparse estimation https://arxiv.org/abs/2605.03134 #stat.ME #stat.ML

In-Sample Evaluation of Subgroups Identified by Generic Machine Learning https://arxiv.org/abs/2605.03141 #stat.ME

Scalable generative modeling of non-Gaussian spatio-temporal fields via autoregressive Gaussian processes https://arxiv.org/abs/2605.03152 #stat.ME

Structure Learning for Directed Trees with Compositional Nodes https://arxiv.org/abs/2605.03178 #stat.ME

Evaluating the probative value of forensic gait analysis evidence using empirical data https://arxiv.org/abs/2605.03193 #stat.AP

A comparative study of two-sample hypothesis tests in the presence of long-term survivors https://arxiv.org/abs/2605.03198 #stat.ME