Papers to Appear in Subsequent Issues

When papers are accepted for publication, they will appear below. Any changes that are made during the production process will only appear in the final version. Papers listed here are not updated during the production process and are removed once an issue is published.

 

A non-asymptotic distributional theory of approximate message passing for sparse and robust regression Gen Li and Yuting Wei
Optimal Integrative Estimation for Distributed Precision Matrices with Heterogeneity Adjustment Yinrui Sun and Yin Xia
Local geometry of high-dimensional mixture models: Effective spectral theory and dynamical transitions Gerard Ben Arous, Reza Gheissari, Jiaoyang Huang and Aukosh Jagannath
High-order Accurate Inference on Manifolds Chengzhu Huang and Anru Zhang
Uniform Estimation and Inference for Nonparametric Partitioning-Based M-Estimators Matias D. Cattaneo, Yingjie Feng and Boris Shigida
DiPMInd: Distance Profile based Mutual Independence testing for random objects Yaqing Chen and Paromita Dubey
Estimating the False Discovery Rate of Variable Selection Yixiang Luo, William Fithian and Lihua Lei
Improved thresholds for e-values Christopher Blier-Wong and Ruodu Wang
Measuring Evidence against Exchangeability and Group Invariance with E-values Nick Koning
Privacy Guarantees in Posterior Sampling under Contamination Shenggang Hu, Louis Aslett, Hongsheng Dai, Murray Pollock and Gareth Owen Roberts
Meta-Learning with Generalized Ridge Regression: High-dimensional Asymptotics, Optimality and Hyper-covariance Estimation Yanhao Jin, Krishnakumar Balasubramanian and Debashis Paul
Unbiased kinetic Langevin Monte Carlo with inexact gradients Neil K. Chada, Benedict Leimkuhler, Daniel Paulin and Peter Archibald Whalley
Association and Independence Test for Random Objects Hang Zhou and Hans-Georg Müller
Inferring diffusivity from killed diffusion Richard Nickl and Fanny Seizilles
Linear methods for  non-linear inverse problems Aad van der Vaart, Geerten Koers and Botond Szabo
No-Regret Generative Modeling via Parabolic Monge-Ampère PDE Nabarun Deb and Tengyuan Liang
On the efficiency of finely stratified experiments Yuehao Bai, Jizhou Liu, Azeem Shaikh and Max Tabord-Meehan
Fast convergence rates for estimating the stationary density in SDEs driven by a fractional Brownian motion with semi-contractive drift Chiara Amorino, Eulalia Nualart, Fabien Panloup and Julian Sieber
Generalized Multivariate Threshold Autoregressive Models with Linearly Partitioned Threshold Space Gan Yuan and Chun Yip Yau
Pseudo-Maximum Likelihood Theory for High-Dimensional Rank One Inference Curtis Grant, Aukosh Jagannath and Justin Ko
Estimation beyond Missing (Completely) at Random Tianyi Ma, Kabir A. Verchand, Thomas B. Berrett, Tengyao Wang and Richard J. Samworth
Fast Mixing of Data Augmentation Algorithms: Bayesian Probit, Logit, and Lasso Regression Holden Lee and Kexin Zhang
Gaussian and non-Gaussian Universality of Data Augmentation Kevin Han Huang, Peter Orbanz and Morgane Austern
Statistical Impossibility and Possibility of Aligning LLMs with Human Preferences: From Condorcet Paradox to Nash Equilibrium Kaizhao Liu, Qi Long, Zhekun Shi, Weijie Su and Jiancong Xiao
Higher-Order Graphon Theory: Fluctuations, Degeneracies, and Inference Anirban Chatterjee, Soham Dan and Bhaswar Bikram Bhattacharya
Data assimilation with the $2D$ Navier-Stokes equations: Optimal Gaussian asymptotics for the posterior measure Dimitri Konen and Richard Nickl
Active Subsampling for Measurement-Constrained M-Estimation of Individualized Thresholds with High-Dimensional Data Jingyi Duan, Lehao Fu and Yang Ning
Gaussian and Bootstrap Approximation for Matching-based Average Treatment Effect Estimators Zhaoyang Shi, Chinmoy Bhattacharjee, Krishnakumar Balasubramanian and Wolfgang Polonik
Granulometric Smoothing on Manifolds Diego Bolón, Rosa María Crujeiras and Alberto Rodríguez-Casal
Sample size and power calculations for causal inference in observational studies Bo Liu, Chengxin Yang and Fan Li
Consistent Infill Estimability of the Regression Slope Between Gaussian Random Fields Under Spatial Confounding Abhirup Datta and Michael L. Stein
Consistent Bayesian Spatial Domain Partitioning Using Predictive Spanning Tree Methods Kun Huang and Huiyan Sang
Local minima of the empirical risk in high dimension: General theorems and convex examples Kiana Asgari, Andrea Montanari and Basil Saeed
Nuisance Function Tuning and Sample Splitting for Optimally Estimating a Doubly Robust Functional Sean McGrath and Rajarshi Mukherjee
Spectral Asymptotics of Neural Network Jacobians: Convergency, Universality, and Phase Transition Huiqin Li, Guangming Pan and Yanqing Yin