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作者：Mandozzi, Jacopo; Buhlmann, Peter
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：We propose a method for testing whether hierarchically ordered groups of potentially correlated variables are significant for explaining a response in a high-dimensional linear model. In presence of highly correlated variables, as is very common in high-dimensional data, it seems indispensable to go beyond an approach of inferring individual regression coefficients, and we show that detecting smallest groups of variables (MTDs: minimal true detections) is realistic. Thanks to the hierarchy amo...
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作者：Jiang, Yuan; He, Yunxiao; Zhang, Heping
作者单位：Oregon State University; Nielsen Holdings Inc.; Yale University; Yale University
摘要：LASSO is a popular statistical tool often used in conjunction with generalized linear models that can simultaneously select variables and estimate parameters. When there are many variables of interest, as in current biological and biomedical studies, the power of LASSO can be limited. Fortunately, so much biological and biomedical data have been collected and they may contain useful information about the importance of certain variables. This article proposes an extension of LASSO, namely, prio...
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作者：Feng, Xingdong; Zhu, Liping
作者单位：Shanghai University of Finance & Economics; Renmin University of China
摘要：In this article, we establish a novel connection between the null hypothesis H-0 on the coefficients and a rank-reducible form of the varying coefficient model in quantile regression. We use B-splines to approximate the varying coefficients in the rank-reducible model, and make use of the fact that the null hypothesis H-0 implies a unidimensional structure of a transformed coefficient matrix for the B-spline basis functions. By evaluating the unidimensional structure, we alleviate the difficul...
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作者：Guo, Jianhua; Hu, Jianchang; Jing, Bing-Yi; Zhang, Zhen
作者单位：Hong Kong University of Science & Technology; National University of Singapore
摘要：We consider a high-dimensional linear regression problem, where the covariates (features) are ordered in some meaningful way, and the number of covariates p can be much larger than the sample size n. The fused lasso of Tibshirani et al. is designed especially to tackle this type of problems; it yields sparse coefficients and selects grouped variables, and encourages local constant coefficient profile within each group. However, in some applications, the effects of different features within a g...
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作者：Liang, Faming; Jin, Ick Hoon; Song, Qifan; Liu, Jun S.
作者单位：State University System of Florida; University of Florida; University of Notre Dame; University System of Ohio; Ohio State University; Purdue University System; Purdue University; Harvard University
摘要：Sampling from the posterior distribution for a model whose normalizing constant is intractable is a long-standing problem in statistical research. We propose a new algorithm, adaptive auxiliary variable exchange algorithm, or, in short, adaptive exchange (AEX) algorithm, to tackle this problem. The new algorithm can be viewed as a MCMC extension of the exchange algorithm, which generates auxiliary variables via an importance sampling procedure from a Markov chain running in parallel. The conve...
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作者：Reimherr, Matthew; Nicolae, Dan
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Chicago
摘要：Quantifying heritability is the first step in understanding the contribution of genetic variation to the risk architecture of complex human diseases and traits. Heritability can be estimated for univariate phenotypes from nonfamily data using linear mixed effects models. There is, however, no fully developed methodology for defining or estimating heritability from longitudinal studies. By examining longitudinal studies, researchers have the opportunity to better understand the genetic influenc...
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作者：Wei, Ying; Song, Xiaoyu; Liu, Mengling; Ionita-Laza, Iuliana; Reibman, Joan
作者单位：Columbia University; Columbia University; New York University; New York University
摘要：Case-control design is widely used in epidemiology and other fields to identify factors associated with a disease. Data collected from existing case-control studies can also provide a cost-effective way to investigate the association of risk factors with secondary outcomes. When the secondary outcome is a continuous random variable, most of the existing methods focus on the statistical inference on the mean of the secondary outcome. In this article, we propose a quantile-based approach to faci...
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作者：Jiang, Ci-Ren; Aston, John A. D.; Wang, Jane-Ling
作者单位：University of Cambridge; University of California System; University of California Davis
摘要：Positron emission tomography (PET) is an imaging technique which can be used to investigate chemical changes in human biological processes such as cancer development or neurochemical reactions. Most dynamic PET scans are currently analyzed based on the assumption that linear first-order kinetics can be used to adequately describe the system under observation. However, there has recently been strong evidence that this is not the case. To provide an analysis of PET data which is free from this c...
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作者：Chatterjee, Nilanjan; Chen, Yi-Hau; Maas, Paige; Carroll, Raymond J.
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作者：Hahn, P. Richard; Murray, Jared S.; Manolopoulou, Ioanna
作者单位：University of Chicago; Carnegie Mellon University; University of London; University College London
摘要：This article describes the use of flexible Bayesian regression models for estimating a partially identified probability function. Our approach permits efficient sensitivity analysis concerning the posterior impact of priors on the partially identified component of the regression model. The new methodology is illustrated on an important problem where only partially observed data are availableinferring the prevalence of accounting misconduct among publicly traded U.S. businesses. Supplementary m...
