Journal of the American Statistical Association

最新文章

• Theory of Stochastic Objects: Probability, Stochastic Processes and Inference. AI翻译
  作者：Behme, Anita D.
  作者单位：Technische Universitat Dresden
• Measuring Agreement: Models, Methods, and Applications. AI翻译
  作者：Ismail, Noor Azina
  作者单位：Universiti Malaya
• Predicting Clinical Outcomes in Glioblastoma: An Application of Topological and Functional Data Analysis AI翻译
  作者：Crawford, Lorin; Monod, Anthea; Chen, Andrew X.; Mukherjee, Sayan; Rabadan, Raul
  作者单位：Brown University; Brown University; Brown University; Tel Aviv University; Columbia University; Duke University; Duke University; Duke University; Duke University
  摘要：Glioblastoma multiforme (GBM) is an aggressive form of human brain cancer that is under active study in the field of cancer biology. Its rapid progression and the relative time cost of obtaining molecular data make other readily available forms of data, such as images, an important resource for actionable measures in patients. Our goal is to use information given by medical images taken from GBM patients in statistical settings. To do this, we design a novel statistic?the smooth Euler characte...
• On Optimal Tests for Rotational Symmetry Against New Classes of Hyperspherical Distributions AI翻译
  作者：Garcia-Portugues, Eduardo; Paindaveine, Davy; Verdebout, Thomas
  作者单位：Universite Libre de Bruxelles; Universite Libre de Bruxelles; Universite de Toulouse; Universite Toulouse 1 Capitole; Toulouse School of Economics
  摘要：Motivated by the central role played by rotationally symmetric distributions in directional statistics, we consider the problem of testing rotational symmetry on the hypersphere. We adopt a semiparametric approach and tackle problems where the location of the symmetry axis is either specified or unspecified. For each problem, we define two tests and study their asymptotic properties under very mild conditions. We introduce two new classes of directional distributions that extend the rotational...
• Fast and Accurate Binary Response Mixed Model Analysis via Expectation Propagation AI翻译
  作者：Hall, P.; Johnstone, I. M.; Ormerod, J. T.; Wand, M. P.; Yu, J. C. F.
  作者单位：University of Melbourne; Stanford University; University of Sydney; University of Technology Sydney
  摘要：Expectation propagation is a general prescription for approximation of integrals in statistical inference problems. Its literature is mainly concerned with Bayesian inference scenarios. However, expectation propagation can also be used to approximate integrals arising in frequentist statistical inference. We focus on likelihood-based inference for binary response mixed models and show that fast and accurate quadrature-free inference can be realized for the probit link case with multivariate ra...
• Functional Censored Quantile Regression AI翻译
  作者：Jiang, Fei; Cheng, Qing; Yin, Guosheng; Shen, Haipeng
  作者单位：University of Hong Kong; National University of Singapore; University of Hong Kong; University of Hong Kong
  摘要：We propose a functional censored quantile regression model to describe the time-varying relationship between time-to-event outcomes and corresponding functional covariates. The time-varying effect is modeled as an unspecified function that is approximated via B-splines. A generalized approximate cross-validation method is developed to select the number of knots by minimizing the expected loss. We establish asymptotic properties of the method and the knot selection procedure. Furthermore, we co...
• Discussion of From Fixed-X to Random-X Regression: Bias-Variance Decompositions, Covariance Penalties, and Prediction Error Estimation AI翻译
  作者：Shen, Xiaotong; Huang, Hsin-Cheng
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; Academia Sinica - Taiwan
• Main Effects and Interactions in Mixed and Incomplete Data Frames AI翻译
  作者：Robin, Genevieve; Klopp, Olga; Josse, Julie; Moulines, Eric; Tibshirani, Robert
  作者单位：Institut Polytechnique de Paris; Ecole Polytechnique; Inria; ESSEC Business School; Institut Polytechnique de Paris; ENSAE Paris; HSE University (National Research University Higher School of Economics); Stanford University; Stanford University
  摘要：A mixed data frame (MDF) is a table collecting categorical, numerical, and count observations. The use of MDF is widespread in statistics and the applications are numerous from abundance data in ecology to recommender systems. In many cases, an MDF exhibits simultaneously main effects, such as row, column, or group effects and interactions, for which a low-rank model has often been suggested. Although the literature on low-rank approximations is very substantial, with few exceptions, existing ...
• Estimation of Heterogeneous Individual Treatment Effects With Endogenous Treatments AI翻译
  作者：Feng, Qian; Vuong, Quang; Xu, Haiqing
  作者单位：University of Texas System; University of Texas Austin; New York University
  摘要：This article estimates individual treatment effects (ITE) and its probability distribution in a triangular model with binary-valued endogenous treatments. Our estimation procedure takes two steps. First, we estimate the counterfactual outcome and hence, the ITE for every observational unit in the sample. Second, we estimate the ITE density function of the whole population. Our estimation method does not suffer from the ill-posed inverse problem associated with inverting a nonlinear functional....
• Bayesian Repulsive Gaussian Mixture Model AI翻译
  作者：Xie, Fangzheng; Xu, Yanxun
  作者单位：Johns Hopkins University
  摘要：We develop a general class of Bayesian repulsive Gaussian mixture models that encourage well-separated clusters, aiming at reducing potentially redundant components produced by independent priors for locations (such as the Dirichlet process). The asymptotic results for the posterior distribution of the proposed models are derived, including posterior consistency and posterior contraction rate in the context of nonparametric density estimation. More importantly, we show that compared to the ind...