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作者：Bolin, David; Lindgren, Finn
作者单位：Chalmers University of Technology; University of Bath
摘要：In several areas of application ranging from brain imaging to astrophysics and geostatistics, an important statistical problem is to find regions where the process studied exceeds a certain level. Estimating such regions so that the probability for exceeding the level in the entire set is equal to some predefined value is a difficult problem connected to the problem of multiple significance testing. In this work, a method for solving this problem, as well as the related problem of finding cred...
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作者：Lian, Heng; Liang, Hua; Carroll, Raymond J.
作者单位：Nanyang Technological University; George Washington University; Texas A&M University System; Texas A&M University College Station
摘要：We consider heteroscedastic regression models where the mean function is a partially linear single-index model and the variance function depends on a generalized partially linear single-index model. We do not insist that the variance function depends only on the mean function, as happens in the classical generalized partially linear single-index model. We develop efficient and practical estimation methods for the variance function and for the mean function. Asymptotic theory for the parametric...
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作者：Hauser, Alain; Buehlmann, Peter
作者单位：University of Bern; Swiss Institute of Bioinformatics; Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：In many applications we have both observational and (randomized) interventional data. We propose a Gaussian likelihood framework for joint modelling of such different data types, based on global parameters consisting of a directed acyclic graph and corresponding edge weights and error variances. Thanks to the global nature of the parameters, maximum likelihood estimation is reasonable with only one or few data points per intervention. We prove consistency of the Bayesian information criterion ...
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作者：Zhang, Weiping; Leng, Chenlei; Tang, Cheng Yong
作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Warwick; National University of Singapore; University of Colorado System; University of Colorado Denver; Children's Hospital Colorado; University of Colorado Anschutz Medical Campus
摘要：In longitudinal studies, it is of fundamental importance to understand the dynamics in the mean function, variance function and correlations of the repeated or clustered measurements. For modelling the covariance structure, Cholesky-type decomposition-based approaches have been demonstrated to be effective. However, parsimonious approaches for directly revealing the correlation structure between longitudinal measurements remain less well explored, and existing joint modelling approaches may en...
