Journal of the American Statistical Association

最新文章

• Conjugate Gradient Methods for High-Dimensional GLMMs AI翻译
  作者：Pandolfi, Andrea; Papaspiliopoulos, Omiros; Zanella, Giacomo
  作者单位：Bocconi University; Bocconi University; Bocconi University
  摘要：Generalized linear mixed models (GLMMs) are a widely used tool in statistical analysis. The main bottleneck of many computational approaches lies in the inversion of the high dimensional precision matrices associated with the random effects. Such matrices are typically sparse; however, the sparsity pattern resembles a multi partite random graph, which does not lend itself well to default sparse linear algebra techniques. Notably, we show that, for typical GLMMs, the Cholesky factor is dense ev...
• Online Policy Learning and Inference by Matrix Completion AI翻译
  作者：Duan, Congyuan; Li, Jingyang; Xia, Dong
  作者单位：Hong Kong University of Science & Technology; University of Michigan System; University of Michigan
  摘要：Is it possible to make online decisions when personalized covariates are unavailable? We take a collaborative-filtering approach for decision-making based on collective preferences. By assuming low-dimensional latent features, we formulate the covariate-free decision-making problem as a matrix completion bandit. We propose a policy learning procedure that combines an epsilon -greedy policy for decision-making with an online gradient descent algorithm for bandit parameter estimation. Our novel ...
• Bayesian Random-Effects Meta-Analysis Integrating Individual Participant Data and Aggregate Data AI翻译
  作者：Huang, Yunxiang; Kim, Hang J.; Huang, Chiung-Yu; Kim, Mi-Ok
  作者单位：University of California System; University of California San Francisco; University System of Ohio; University of Cincinnati
  摘要：Meta-analysis using individual participant data (IPD) offers many benefits, including greater analytical flexibility, compared to conventional analyses based on aggregate data (AD). However, it is often hindered by restricted access to IPD. Relying solely on available IPD may introduce data availability bias, compromising external validity. Integrating IPD with relevant AD addresses this concern, but existing methods are restrictive, requiring precise knowledge of the IPD-to-AD parameter mappi...
• Higher-Order Accurate Two-Sample Network Inference and Network Hashing AI翻译
  作者：Shao, Meijia; Xia, Dong; Zhang, Yuan; Wu, Qiong; Chen, Shuo
  作者单位：University System of Ohio; Ohio State University; Hong Kong University of Science & Technology; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; University System of Maryland; University of Maryland Baltimore
  摘要：Two-sample hypothesis testing for network comparison presents many significant challenges, including: leveraging repeated network observations and known node registration, but without requiring them to operate; relaxing strong structural assumptions; achieving finite-sample higher-order accuracy; handling different network sizes and sparsity levels; fast computation and memory parsimony; controlling false discovery rate (FDR) in multiple testing; and theoretical understandings, particularly re...
• Checking the Cox Proportional Hazards Model with Interval-Censored Data AI翻译
  作者：Xu, Yangjianchen; Zeng, Donglin; Lin, D. Y.
  作者单位：University of Waterloo; University of Michigan System; University of Michigan; University of North Carolina; University of North Carolina Chapel Hill
  摘要：This article presents a general framework for checking the adequacy of the Cox proportional hazards model with interval-censored data, which arise when the event of interest is known only to occur over a random time interval. Specifically, we construct certain stochastic processes that are informative about various aspects of the model, that is, the functional forms of covariates, the exponential link function and the proportional hazards assumption. We establish their weak convergence to zero...
• An Economical Approach to Design Posterior Analyses AI翻译
  作者：Hagar, Luke; Stevens, Nathaniel T.
  作者单位：McGill University; University of Waterloo
  摘要：To design Bayesian studies, criteria for the operating characteristics of posterior analyses-such as power and the Type I error rate-are often assessed by estimating sampling distributions of posterior probabilities via simulation. In this article, we propose an economical method to determine optimal sample sizes and decision criteria for such studies. Using our theoretical results that model posterior probabilities as a function of the sample size, we assess operating characteristics througho...
• Phase-Type Distributions for Sieve Estimation AI翻译
  作者：Hu, Xiangbin; Wang, Yudong; Ye, Zhisheng; Zhao, Xingqiu
  作者单位：National University of Singapore; Hong Kong Polytechnic University
  摘要：In many semiparametric models, the infinite-dimensional parameter of direct interest is a probability density, but its nonparametric estimation is usually difficult in the presence of incomplete data. To address this issue, this study promotes phase-type distributions as a method of sieve. Phase-type distributions are dense in the space of nonnegative distributions, closed under minimum, maximum, and convolution, and compatible with the accelerated failure time model. This renders them attract...
• Comparison of Longitudinal Trajectories Using a High-Dimensional Partial Linear Semiparametric Mixed-Effects Model AI翻译
  作者：Leon, Sami; Wu, Tong Tong
  作者单位：University of Rochester
  摘要：In longitudinal research, it is essential to compare sets of trajectories, commonly seen as changes over time in different treatment or patient groups. This article presents a partial linear semiparametric mixed-effects model (PLSMM) for the analysis and comparison of nonlinear longitudinal trajectories with high-dimensional covariates across groups. Our flexible modeling framework can effectively handle complex temporal effects and extensive data while providing statistical inference. This me...
• Nonparametric Predictive Inference for Discrete Data via Metropolis-Adjusted Dirichlet Sequences AI翻译
  作者：Agnoletto, Davide; Rigon, Tommaso; Dunson, David B.
  作者单位：Duke University; University of Milano-Bicocca
  摘要：This article is motivated by challenges in conducting Bayesian inferences on unknown discrete distributions, with a particular focus on count data. To avoid the computational disadvantages of traditional mixture models, we develop a novel Bayesian predictive approach. In particular, our Metropolis-adjusted Dirichlet (mad) sequence model characterizes the predictive measure as a mixture of a base measure and Metropolis-Hastings kernels centered on previous data points. The resulting mad sequenc...
• Structured Conformal Inference for Matrix Completion with Applications to Group Recommender Systems AI翻译
  作者：Liang, Ziyi; Xie, Tianmin; Tong, Xin; Sesia, Matteo
  作者单位：University of California System; University of California Irvine; University of Southern California; University of Hong Kong; University of Southern California
  摘要：We develop a conformal inference method to construct joint prediction regions for structured groups of missing entries in a sparsely observed matrix, focusing on groups drawn from the same column. The method can be combined with any black-box matrix completion algorithm and makes no distributional assumptions for the underlying data matrix; instead, it obtains rigorous inferences by modeling the missingness mechanism. In the context of recommender systems, for example, it is useful to quantify...