Journal of the American Statistical Association

最新文章

• Sparse Sliced Inverse Regression via Lasso AI翻译
  作者：Lin, Qian; Zhao, Zhigen; Liu, Jun S.
  作者单位：Tsinghua University; Tsinghua University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University; Harvard University
  摘要：For multiple index models, it has recently been shown that the sliced inverse regression (SIR) is consistent for estimating the sufficient dimension reduction (SDR) space if and only if , where p is the dimension and n is the sample size. Thus, when p is of the same or a higher order of n, additional assumptions such as sparsity must be imposed in order to ensure consistency for SIR. By constructing artificial response variables made up from top eigenvectors of the estimated conditional covari...
• Dynamic Tensor Clustering AI翻译
  作者：Sun, Will Wei; Li, Lexin
  作者单位：University of Miami; University of California System; University of California Berkeley
  摘要：Dynamic tensor data are becoming prevalent in numerous applications. Existing tensor clustering methods either fail to account for the dynamic nature of the data, or are inapplicable to a general-order tensor. There is also a gap between statistical guarantee and computational efficiency for existing tensor clustering solutions. In this article, we propose a new dynamic tensor clustering method that works for a general-order dynamic tensor, and enjoys both strong statistical guarantee and high...
• Combined Hypothesis Testing on Graphs With Applications to Gene Set Enrichment Analysis AI翻译
  作者：Wang, Shulei; Yuan, Ming
  作者单位：University of Wisconsin System; University of Wisconsin Madison; Columbia University
  摘要：Motivated by gene set enrichment analysis, we investigate the problem of combined hypothesis testing on a graph. A general framework is introduced to make effective use of the structural information of the underlying graph when testing multivariate means. A new testing procedure is proposed within this framework, and shown to be optimal in that it can consistently detect departures from the collective null at a rate that no other test could improve, for almost all graphs. We also provide gener...
• Proper Inference for Value Function in High-Dimensional Q-Learning for Dynamic Treatment Regimes AI翻译
  作者：Zhu, Wensheng; Zeng, Donglin; Song, Rui
  作者单位：Northeast Normal University - China; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; North Carolina State University
  摘要：Dynamic treatment regimes are a set of decision rules and each treatment decision is tailored over time according to patients' responses to previous treatments as well as covariate history. There is a growing interest in development of correct statistical inference for optimal dynamic treatment regimes to handle the challenges of nonregularity problems in the presence of nonrespondents who have zero-treatment effects, especially when the dimension of the tailoring variables is high. In this ar...
• Causal Interaction in Factorial Experiments: Application to Conjoint Analysis AI翻译
  作者：Egami, Naoki; Imai, Kosuke
  作者单位：Princeton University; Harvard University; Harvard University; Princeton University
  摘要：We study causal interaction in factorial experiments, in which several factors, each with multiple levels, are randomized to form a large number of possible treatment combinations. Examples of such experiments include conjoint analysis, which is often used by social scientists to analyze multidimensional preferences in a population. To characterize the structure of causal interaction in factorial experiments, we propose a new causal interaction effect, called the average marginal interaction e...
• Nonparametric Causal Effects Based on Incremental Propensity Score Interventions AI翻译
  作者：Kennedy, Edward H.
  作者单位：Carnegie Mellon University
  摘要：Most work in causal inference considers deterministic interventions that set each unit's treatment to some fixed value. However, under positivity violations these interventions can lead to nonidentification, inefficiency, and effects with little practical relevance. Further, corresponding effects in longitudinal studies are highly sensitive to the curse of dimensionality, resulting in widespread use of unrealistic parametric models. We propose a novel solution to these problems: incremental in...
• Stochastic Quasi-Likelihood for Case-Control Point Pattern Data AI翻译
  作者：Xu, Ganggang; Waagepetersen, Rasmus; Guan, Yongtao
  作者单位：State University of New York (SUNY) System; Binghamton University, SUNY; University of Miami; Aalborg University
  摘要：We propose a novel stochastic quasi-likelihood estimation procedure for case-control point processes. Quasi-likelihood for point processes depends on a certain optimal weight function and for the new method the weight function is stochastic since it depends on the control point pattern. The new procedure also provides a computationally efficient implementation of quasi-likelihood for univariate point processes in which case a synthetic control point process is simulated by the user. Under mild...
• Accurate and Efficient P-value Calculation Via Gaussian Approximation: A Novel Monte-Carlo Method AI翻译
  作者：Liu, Yaowu; Xie, Jun
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Purdue University System; Purdue University
  摘要：It is of fundamental interest in statistics to test the significance of a set of covariates. For example, in genome-wide association studies, a joint null hypothesis of no genetic effect is tested for a set of multiple genetic variants. The minimum p-value method, higher criticism, and Berk-Jones tests are particularly effective when the covariates with nonzero effects are sparse. However, the correlations among covariates and the nonGaussian distribution of the response pose a great challenge...
• Bayesian Graphical Regression AI翻译
  作者：Ni, Yang; Stingo, Francesco C.; Baladandayuthapani, Veerabhadran
  作者单位：University of Texas System; University of Texas Austin; Rice University; University of Texas System; UTMD Anderson Cancer Center; University of Florence
  摘要：We consider the problem of modeling conditional independence structures in heterogenous data in the presence of additional subject-level covariatestermed graphical regression. We propose a novel specification of a conditional (in)dependence function of covariateswhich allows the structure of a directed graph to vary flexibly with the covariates; imposes sparsity in both edge and covariate selection; produces both subject-specific and predictive graphs; and is computationally tractable. We prov...
• Covariate Information Matrix for Sufficient Dimension Reduction AI翻译
  作者：Yao, Weixin; Nandy, Debmalya; Lindsay, Bruce G.; Chiaromonte, Francesca
  作者单位：University of California System; University of California Riverside; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Scuola Superiore Sant'Anna; Scuola Superiore Sant'Anna
  摘要：Building upon recent research on the applications of the density information matrix, we develop a tool for sufficient dimension reduction (SDR) in regression problems called covariate information matrix (CIM). CIM exhaustively identifies the central subspace (CS) and provides a rank ordering of the reduced covariates in terms of their regression information. Compared to other popular SDR methods, CIM does not require distributional assumptions on the covariates, or estimation of the mean regre...