542期

最新文章

• Inference for High-Dimensional Censored Quantile Regression AI翻译
  作者：Fei, Zhe; Zheng, Qi; Hong, Hyokyoung G.; Li, Yi
  作者单位：University of California System; University of California Los Angeles; University of Louisville; Michigan State University; University of Michigan System; University of Michigan
  摘要：With the availability of high-dimensional genetic biomarkers, it is of interest to identify heterogeneous effects of these predictors on patients' survival, along with proper statistical inference. Censored quantile regression has emerged as a powerful tool for detecting heterogeneous effects of covariates on survival outcomes. To our knowledge, there is little work available to draw inferences on the effects of high-dimensional predictors for censored quantile regression (CQR). This article p...
• Evaluating Association Between Two Event Time Observations Subject to informative Censoring AI翻译
  作者：Li, Dongdong; Hu, X. Joan; Wang, Rui
  作者单位：Harvard Pilgrim Health Care; Harvard University; Harvard Medical School; Simon Fraser University; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：This article is concerned with evaluating the association between two event times without specifying the joint distribution parametrically. This is particularly challenging when the observations on the event times are subject to informative censoring due to a terminating event such as death. There are few methods suitable for assessing covariate effects on association in this context. We link the joint distribution of the two event times and the informative censoring time using a nested copula...
• Model-Robust Inference for Clinical Trials that Improve Precision by Stratified Randomization and Covariate Adjustment AI翻译
  作者：Wang, Bingkai; Susukida, Ryoko; Mojtabai, Ramin; Amin-Esmaeili, Masoumeh; Rosenblum, Michael
  作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Tehran University of Medical Sciences
  摘要：Two commonly used methods for improving precision and power in clinical trials are stratified randomization and covariate adjustment. However, many trials do not fully capitalize on the combined precision gains from these two methods, which can lead to wasted resources in terms of sample size and trial duration. We derive consistency and asymptotic normality of model-robust estimators that combine these two methods, and showthat these estimators can lead to substantial gains in precision and p...
• Multivariate Temporal Point Process Regression AI翻译
  作者：Tang, Xiwei; Li, Lexin
  作者单位：University of Virginia; University of California System; University of California Berkeley
  摘要：Point process modeling is gaining increasing attention, as point process type data are emerging in a large variety of scientific applications. In this article, motivated by a neuronal spike trains study, we propose a novel point process regression model, where both the response and the predictor can be a high-dimensional point process. We model the predictor effects through the conditional intensities using a set of basis transferring functions in a convolutional fashion. We organize the corre...
• Hamiltonian-Assisted Metropolis Sampling AI翻译
  作者：Song, Zexi; Tan, Zhiqiang
  作者单位：Rutgers University System; Rutgers University New Brunswick
  摘要：Various Markov chain Monte Carlo (MCMC) methods are studied to improve upon random walk Metropolis sampling, for simulation from complex distributions. Examples include Metropolis-adjusted Langevin algorithms, Hamiltonian Monte Carlo, and other algorithms related to underdamped Langevin dynamics. We propose a broad class of irreversible sampling algorithms, called Hamiltonian-assisted Metropolis sampling (HAMS), and develop two specific algorithms with appropriate tuning and preconditioning st...
• Empirical Bayes Mean Estimation With Nonparametric Errors Via Order Statistic Regression on Replicated Data AI翻译
  作者：Ignatiadis, Nikolaos; Saha, Sujayam; Sun, Dennis L.; Muralidharan, Omkar
  作者单位：Stanford University; Alphabet Inc.; Google Incorporated; California State University System; California Polytechnic State University San Luis Obispo
  摘要：We study empirical Bayes estimation of the effect sizes of N units from K noisy observations on each unit. We show that it is possible to achieve near-Bayes optimal mean squared error, without any assumptions or knowledge about the effect size distribution or the noise. The noise distribution can be heteroscedastic and vary arbitrarily from unit to unit. Our proposal, which we call Aurora, leverages the replication inherent in the K observations per unit and recasts the effect size estimation ...
• GEE-Assisted Variable Selection for Latent Variable Models with Multivariate Binary Data AI翻译
  作者：Hui, Francis K. C.; Muller, Samuel; Welsh, A. H.
  作者单位：Australian National University; University of Sydney
  摘要：Multivariate data are commonly analyzed using one of two approaches: a conditional approach based on generalized linear latent variable models (GLLVMs) or some variation thereof, and a marginal approach based on generalized estimating equations (GEEs). With research on mixed models and GEEs having gone down separate paths, there is a common mindset to treat the two approaches as mutually exclusive, with which to use driven by the question of interest. In this article, focusing on multivariate ...
• Rerandomization in Stratified Randomized Experiments AI翻译
  作者：Wang, Xinhe; Wang, Tingyu; Liu, Hanzhong
  作者单位：Tsinghua University; Tsinghua University; Tsinghua University
  摘要：Stratification and rerandomization are two well-known methods used in randomized experiments for balancing the baseline covariates. Renowned scholars in experimental design have recommended combining these two methods; however, limited studies have addressed the statistical properties of this combination. This article proposes two rerandomization methods to be used in stratified randomized experiments, based on the overall and stratum-specific Mahalanobis distances. The first method is applica...
• Functional Estimation and Change Detection for Nonstationary Time Series AI翻译
  作者：Mies, Fabian
  作者单位：RWTH Aachen University
  摘要：Tests for structural breaks in time series should ideally be sensitive to breaks in the parameter of interest, while being robust to nuisance changes. Statistical analysis thus needs to allow for some form of nonstationarity under the null hypothesis of no change. In this article, estimators for integrated parameters of locally stationary time series are constructed and a corresponding functional central limit theorem is established, enabling change-point inference for a broad class of paramet...
• Statistical Inference for High-Dimensional Generalized Linear Models With Binary Outcomes AI翻译
  作者：Cai, T. Tony; Guo, Zijian; Ma, Rong
  作者单位：University of Pennsylvania; Rutgers University System; Rutgers University New Brunswick; Stanford University
  摘要：This article develops a unified statistical inference framework for high-dimensional binary generalized linear models (GLMs) with general link functions. Both unknown and known design distribution settings are considered. A two-step weighted bias-correction method is proposed for constructing confidence intervals (CIs) and simultaneous hypothesis tests for individual components of the regression vector. Minimax lower bound for the expected length is established and the proposed CIs are shown t...