Journal of the American Statistical Association

最新文章

• General Forms of Finite Population Central Limit Theorems with Applications to Causal Inference AI翻译
  作者：Li, Xinran; Ding, Peng
  作者单位：Harvard University; University of California System; University of California Berkeley
  摘要：Frequentists' inference often delivers point estimators associated with confidence intervals or sets for parameters of interest. Constructing the confidence intervals or sets requires understanding the sampling distributions of the point estimators, which, in many but not all cases, are related to asymptotic Normal distributions ensured by central limit theorems. Although previous literature has established various forms of central limit theorems for statistical inference in super population m...
• A Semiparametric Single-Index Risk Score Across Populations AI翻译
  作者：Ma, Shujie; Ma, Yanyuan; Wang, Yanqing; Kravitz, Eli S.; Carroll, Raymond J.
  作者单位：University of California System; University of California Riverside; University of South Carolina System; University of South Carolina Columbia; Fred Hutchinson Cancer Center; Texas A&M University System; Texas A&M University College Station; University of Technology Sydney
  摘要：We consider a problem motivated by issues in nutritional epidemiology, across diseases and populations. In this area, it is becoming increasingly common for diseases to be modeled by a single diet score, such as the Healthy Eating Index, the Mediterranean Diet Score, etc. For each disease and for each population, a partially linear single-indexmodel is fit. The partially linear aspect of the problem is allowed to differ in each population and disease. However, and crucially, the single-index i...
• A Framework for Synthetic Control Methods With High-Dimensional, Micro-Level Data: Evaluating a Neighborhood-Specific Crime Intervention AI翻译
  作者：Robbins, Michael W.; Saunders, Jessica; Kilmer, Beau
  作者单位：RAND Corporation; RAND Corporation
  摘要：The synthetic control method is an increasingly popular tool for analysis of program efficacy. Here, it is applied to a neighborhood-specific crime intervention in Roanoke, VA, and several novel contributions are made to the synthetic control toolkit. We examine high-dimensional data at a granular level (the treated area has several cases, a large number of untreated comparison cases, and multiple outcome measures). Calibration is used to develop weights that exactly match the synthetic contro...
• Estimating Population Size With Link-Tracing Sampling AI翻译
  作者：Vincent, Kyle; Thompson, Steve
  作者单位：Bank of Canada; Simon Fraser University
  摘要：We present a new design and method for estimating the size of a hidden population best reached through a link-tracing design. The design is based on selecting initial samples at random and then adaptively tracing links to add new members. The inferential procedure involves the Rao-Blackwell theorem applied to a sufficient statistic markedly different from the usual one that arises in sampling from a finite population. The strategy involves a combination of link-tracing and mark-recapture estim...
• Independent Component Analysis via Distance Covariance AI翻译
  作者：Matteson, David S.; Tsay, Ruey S.
  作者单位：Cornell University; University of Chicago
  摘要：This article introduces a novel statistical framework for independent component analysis (ICA) of multivariate data. We propose methodology for estimating mutually independent components, and a versatile resampling-based procedure for inference, including misspecification testing. Independent components are estimated by combining a nonparametric probability integral transformation with a generalized non parametric whitening method based on distance covariance that simultaneously minimizes all ...
• Essentials of Probability Theory for Statisticians AI翻译
  作者：Veraart, Almut E. D.
  作者单位：Imperial College London
• Statistical Tests for Large Tree-Structured Data AI翻译
  作者：Bharath, Karthik; Kambadur, Prabhanjan; Dey, Dipak K.; Rao, Arvind; Baladandayuthapani, Veerabhadran
  作者单位：University of Nottingham; Bloomberg L.P.; University of Connecticut; University of Texas System; UTMD Anderson Cancer Center; University of Texas System; UTMD Anderson Cancer Center
  摘要：We develop a general statistical framework for the analysis and inference of large tree-structured data, with a focus on developing asymptotic goodness-of-fit tests. We first propose a consistent statistical model for binary trees, from which we develop a class of invariant tests. Using the model for binary trees, we then construct tests for general trees by using the distributional properties of the continuum random tree, which arises as the invariant limit for a broad class of models for tre...
• Parsimonious Tensor Response Regression AI翻译
  作者：Li, Lexin; Zhang, Xin
  作者单位：University of California System; University of California Berkeley; State University System of Florida; Florida State University
  摘要：Aiming at abundant scientific and engineering data with not only high dimensionality but also complex structure, we study the regression problem with a multidimensional array (tensor) response and a vector predictor. Applications include, among others, comparing tensor images across groups after adjusting for additional covariates, which is of central interest in neuroimaging analysis. We propose parsimonious tensor response regression adopting a generalized sparsity principle. It models all v...
• Fast Approximate Inference for Arbitrarily Large Semiparametric Regression Models via Message Passing Rejoinder AI翻译
  作者：Wand, M. P.
  作者单位：University of Technology Sydney; Queensland University of Technology (QUT)
• Linear Model Selection When Covariates Contain Errors AI翻译
  作者：Zhang, Xinyu; Wang, Haiying; Ma, Yanyuan; Carroll, Raymond J.
  作者单位：Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; University System Of New Hampshire; University of New Hampshire; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Texas A&M University System; Texas A&M University College Station; University of Technology Sydney
  摘要：Prediction precision is arguably the most relevant criterion of amodel in practice and is often a sought after property. A common difficulty with covariates measured with errors is the impossibility of performing prediction evaluation on the data even if a model is completely given without any unknown parameters. We bypass this inherent difficulty by using special properties on moment relations in linear regression models with measurement errors. The end product is a model selection procedure ...