Journal of the American Statistical Association

最新文章

• 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...
• Deep Regression Learning with Optimal Loss Function AI翻译
  作者：Wang, Xuancheng; Zhou, Ling; Lin, Huazhen
  作者单位：Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China
  摘要：In this article, we develop a novel efficient and robust nonparametric regression estimator under a framework of a feedforward neural network (FNN). There are several interesting characteristics for the proposed estimator. First, the loss function is built upon an estimated maximum likelihood function, which integrates the information from observed data as well as the information from the data distribution. Consequently, the resulting estimator has desirable optimal properties, such as efficie...
• Operationalizing Legislative Bodies: A Methodological and Empirical Perspective with a Bayesian Approach AI翻译
  作者：Luque, Carolina; Sosa, Juan
  作者单位：Universidad EAN; Universidad Nacional de Colombia
  摘要：This article extensively reviews applications, extensions, and models derived from the Bayesian ideal point estimator. We focus our attention on studies conducted in the United States as well as Latin America. First, we provide a detailed description of the Bayesian ideal point estimator. Next, we propose a new taxonomy to synthesize and frame technical developments and applications associated with the estimator in the context of the United States Congress and Latin American governing bodies. ...
• Optimal Network Membership Estimation under Severe Degree Heterogeneity AI翻译
  作者：Ke, Zheng Tracy; Wang, Jingming
  作者单位：Harvard University; University of Virginia
  摘要：Real networks often have severe degree heterogeneity, with maximum, average, and minimum node degrees differing significantly. This article examines the impact of degree heterogeneity on statistical limits of network data analysis. Introducing the heterogeneity distribution (HD) under a degree-corrected mixed membership model, we show that the optimal rate of mixed membership estimation is an explicit functional of the HD. This result confirms that severe degree heterogeneity decelerates the e...
• Debiased Calibration Estimation Using Generalized Entropy in Survey Sampling AI翻译
  作者：Kwon, Yonghyun; Kim, Jae Kwang; Qiu, Yumou
  作者单位：Iowa State University; Peking University; Peking University
  摘要：Incorporating auxiliary information into the survey estimation is a fundamental problem in survey sampling. Calibration weighting is a widely used technique to integrate such information by adjusting design weights to meet benchmarking constraints. Traditional methods, such as those proposed by Deville and S & auml;rndal, solve this problem by minimizing a distance between calibrated and design weights. In this article, we propose a novel calibration framework that instead maximizes a generali...
• Integrative Analysis of Microbial 16S Gene and Shotgun Metagenomic Sequencing Data Improves Statistical Efficiency in Testing Differential Abundance AI翻译
  作者：Yue, Ye; Mao, Yicong; Read, Timothy D.; Fedirko, Veronika; Satten, Glen A.; Chen, Xuan; Zhan, Xiang; Hu, Yi-Juan
  作者单位：Emory University; Peking University; Emory University; University of Texas System; UTMD Anderson Cancer Center; Emory University; Emory University; Huazhong Agricultural University; Southeast University - China; Peking University; Peking University
  摘要：The most widely used technologies for profiling microbial communities are 16S marker-gene sequencing and shotgun metagenomic sequencing. Surprisingly, many microbiome studies have performed both experiments on the same cohort of samples. The two sequencing datasets often reveal consistent patterns of microbial signatures, suggesting that an integrative analysis of both datasets could enhance the testing power for these signatures. However, differential experimental biases, partially overlappin...