Journal of the American Statistical Association

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• Robust and Efficient One-Way MANOVA Tests AI翻译
  作者：Van Aelst, Stefan; Willems, Gert
  作者单位：Ghent University
  摘要：We propose robust tests as alternatives to the classical Wilks' Lambda test in one-way MANOVA. The robust tests use highly robust and efficient multisample multivariate S-estimators or MM-estimators instead of the empirical covariances. The properties of several robust test statistics are compared. Under the null hypothesis, the distribution of the test statistics is proportional to a chi-square distribution. As an alternative to the asymptotic distribution, we develop a fast robust bootstrap ...
• A Bayesian Semiparametric Approach to Intermediate Variables in Causal Inference AI翻译
  作者：Schwartz, Scott L.; Li, Fan; Mealli, Fabrizia
  作者单位：Texas A&M University System; Texas A&M University College Station; Texas A&M AgriLife Research; Duke University; University of Florence
  摘要：In causal inference studies, treatment comparisons often need to be adjusted for confounded post-treatment variables. Principal stratification (PS) is a framework to deal with such variables within the potential outcome approach to causal inference. Continuous intermediate variables introduce inferential challenges to PS analysis. Existing methods either dichotomize the intermediate variable, or assume a fully parametric model for the joint distribution of the potential intermediate variables....
• Some Approximate Evidence Factors in Observational Studies AI翻译
  作者：Rosenbaum, Paul R.
  作者单位：University of Pennsylvania
  摘要：An observational study or nonrandomized experiment has exact evidence factors if it permits several statistically independent tests of the same null hypothesis Ho of no treatment effect, where these several tests depend upon different assumptions about bias from nonrandom treatment assignment. In an observational study, we are typically uncertain about what assumptions truly describe treatment assignment. If independent tests each reject Ho, and if each of these tests is valid under assumption...
• Population Value Decomposition, a Framework for the Analysis of Image Populations AI翻译
  作者：Crainiceanu, Ciprian M.; Caffo, Brian S.; Luo, Sheng; Zipunnikov, Vadim M.; Punjabi, Naresh M.
  作者单位：Johns Hopkins University; University of Texas System; University of Texas Health Science Center Houston; University of Texas School Public Health; Johns Hopkins University
  摘要：Images, often stored in multidimensional arrays, are fast becoming ubiquitous in medical and public health research. Analyzing populations of images is a statistical problem that raises a host of daunting challenges. The most significant challenge is the massive size of the datasets incorporating images recorded for hundreds or thousands of subjects at multiple visits. We introduce the population value decomposition (PVD), a general method for simultaneous dimensionality reduction of large pop...
• VIF Regression: A Fast Regression Algorithm for Large Data AI翻译
  作者：Lin, Dongyu; Foster, Dean P.; Ungar, Lyle H.
  作者单位：University of Pennsylvania; University of Pennsylvania; University of Pennsylvania
  摘要：We propose a fast and accurate algorithm. VIF regression, for doing feature selection in large regression problems. VIP regression is extremely fast: it uses a one-pass search over the predictors and a computationally efficient method of testing each potential predictor for addition to the model. VIE regression provably avoids model overfitting, controlling the marginal false discovery rate. Numerical results show that it is much faster than any other published algorithm for regression with fe...
• Consistent Model Selection for Marginal Generalized Additive Model for Correlated Data AI翻译
  作者：Xue, Lan; Qu, Annie; Zhou, Jianhui
  作者单位：Oregon State University; University of Illinois System; University of Illinois Urbana-Champaign; University of Virginia
  摘要：We consider the generalized additive model when responses from the same cluster are correlated. Incorporating correlation in the estimation of nonparametric components for the generalized additive model is important because it improves estimation efficiency and increases statistical power for model selection. In our setting, there is no specified likelihood function for the generalized additive model, because the outcomes could be nonnormal and discrete, which makes estimation and model select...
• Semiparametric Efficient Estimation for a Class of Generalized Proportional Odds Cure Models AI翻译
  作者：Mao, Meng; Wang, Jane-Ling
  作者单位：University of California System; University of California Davis
  摘要：We present a mixture cure model with the survival time of the uncured group coming from a class of linear transformation models, which is an extension of the proportional odds model This class of model. first proposed by Dabrowska and Doksum ( 988). which we term generalized proportional odds model, is well suited for the mixture cure model setting due to a clear separation between long-term and short-term effects A standard expectation-maximization algorithm can he employed to locate the nonp...
• Multiple Change-Point Estimation With a Total Variation Penalty AI翻译
  作者：Harchaoui, Z.; Levy-Leduc, C.
  作者单位：Inria; Communaute Universite Grenoble Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble Alpes (UGA); Centre National de la Recherche Scientifique (CNRS); Inria; Centre National de la Recherche Scientifique (CNRS); IMT - Institut Mines-Telecom; Institut Polytechnique de Paris; Telecom Paris
  摘要：We propose a new approach for dealing with the estimation of the location of change-points in one-dimensional piecewise constant signals observed in white noise. Our approach consists in reframing this task in a variable selection context. We use a penalized least-square criterion with a L-1-type penalty for this purpose. We explain how to implement this method in practice by using the LARS/LASSO algorithm. We then prove that, in an appropriate asymptotic framework, this method provides consis...
• Group Comparison of Eigenvalues and Eigenvectors of Diffusion Tensors AI翻译
  作者：Schwartzman, Armin; Dougherty, Robert F.; Taylor, Jonathan E.
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute; Stanford University; Stanford University
  摘要：Diffusion tensor imaging (DID data differ from most medical images in that values at each voxel are not scalars, but 3 x 3 symmetric positive definite matrices called diffusion tensors (DTs). The anatomic characteristics of the tissue at each voxel are reflected by the DT eigenvalues and eigenvectors. In this article we consider the problem of testing whether the means of two groups of DT images are equal at each voxel in terms of the DT's eigenvalues, eigenvectors, or both. Because eigendecom...
• An Association Test for Multiple Traits Based on the Generalized Kendall's Tau AI翻译
  作者：Zhang, Heping; Liu, Ching-Ti; Wang, Xueqin
  作者单位：Yale University; Boston University; Sun Yat Sen University; Sun Yat Sen University
  摘要：In many genetics studies, especially in the investigation of mental illness and behavioral disorders, it is common for researchers to collect multiple phenotypes to characterize the complex disease of interest. It may be advantageous to analyze those phenotypic measurements simultaneously if they share a similar genetic mechanism. In this study, we present a nonparametric approach to studying multiple traits together rather than examining each trait separately. Through simulation we compared t...