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作者：Jiang, Jiming; Thuan Nguyen; Rao, J. Sunil
作者单位：University of California System; University of California Davis; Oregon Health & Science University; University of Miami
摘要：We propose a procedure associated with the idea of the E-M algorithm for model selection in the presence of missing data. The idea extends the concept of parameters to include both the model and the parameters under the model, and thus allows the model to be part of the E-M iterations. We develop the procedure, known as the E-MS algorithm, under the assumption that the class of candidate models is finite. Some special cases of the procedure are considered, including E-MS with the generalized i...
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作者：Volfovsky, Alexander; Hoff, Peter D.
作者单位：University of Washington; University of Washington Seattle
摘要：Relational data are often represented as a square matrix, the entries of which record the relationships between pairs of objects. Many statistical methods for the analysis of such data assume some degree of similarity or dependence between objects in terms of the way they relate to each other. However, formal tests for such dependence have not been developed. We provide a test for such dependence using the framework of the matrix normal model, a type of multivariate normal distribution paramet...
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作者：Cao, Hongyuan; Churpek, Mathew M.; Zeng, Donglin; Fine, Jason P.
作者单位：University of Missouri System; University of Missouri Columbia; University of Chicago; University of Chicago; University of North Carolina; University of North Carolina Chapel Hill
摘要：Regression analysis of censored failure observations via the proportional hazards model permits time-varying covariates that are observed at death times. In practice, such longitudinal covariates are typically sparse and only measured at infrequent and irregularly spaced follow-up times. Full likelihood analyses of joint models for longitudinal and survival data impose stringent modeling assumptions that are difficult to verify in practice and that are complicated both inferentially and comput...
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作者：Wikle, Christopher K.; Holan, Scott H.
作者单位：University of Missouri System; University of Missouri Columbia
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作者：Zhu, Jian; Raghunathan, Trivellore E.
作者单位：University of Michigan System; University of Michigan
摘要：A sequential regression or chained equations imputation approach uses a Gibbs sampling-type iterative algorithm that imputes the missing values using a sequence of conditional regression models. It is a flexible approach for handling different types of variables and complex data structures. Many simulation studies have shown that the multiple imputation inferences based on this procedure have desirable repeated sampling properties. However, a theoretical weakness of this approach is that the s...
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作者：Sun, Wei; Liu, Yufeng; Crowley, James J.; Chen, Ting-Huei; Zhou, Hua; Chu, Haitao; Huang, Shunping; Kuan, Pei-Fen; Li, Yuan; Miller, Darla; Shaw, Ginger; Wu, Yichao; Zhabotynsky, Vasyl; McMillan, Leonard; Zou, Fei; Sullivan, Patrick F.; de Villena, Fernando Pardo-Manuel
作者单位：University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina; University of North Carolina Chapel Hill; North Carolina State University; University of Minnesota System; University of Minnesota Twin Cities; University of North Carolina; University of North Carolina Chapel Hill
摘要：We have developed a statistical method named IsoDOT to assess differential isoform expression (DIE) and differential isoform usage (DIU) using RNA-seq data. Here isoform usage refers to relative isoform expression given the total expression of the corresponding gene. IsoDOT performs two tasks that cannot be accomplished by existing methods: to test DIE/DIU with respect to a continuous covariate, and to test DIE/DIU for one case versus one control. The latter task is not an uncommon situation i...
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作者：Chen, Kehui; Lei, Jing
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Carnegie Mellon University
摘要：We propose localized functional principal component analysis (LFPCA), looking for orthogonal basis functions with localized support regions that explain most of the variability of a random process. The LFPCA is formulated as a convex optimization problem through a novel deflated Fantope localization method and is implemented through an efficient algorithm to obtain the global optimum. We prove that the proposed LFPCA converges to the original functional principal component analysis (FPCA) when...
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作者：Pan, Deng; He, Haijin; Song, Xinyuan; Sun, Liuquan
作者单位：Huazhong University of Science & Technology; Chinese University of Hong Kong; Chinese Academy of Sciences
摘要：We propose an additive hazards model with latent variables to investigate the observed and latent risk factors of the failure time of interest. Each latent risk factor is characterized by correlated observed variables through a confirmatory factor analysis model. We develop a hybrid procedure that combines the expectation maximization (EM) algorithm and the borrow-strength estimation approach to estimate the model parameters. We establish the consistency and asymptotic normality of the paramet...
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作者：Vermeulen, Karel; Vansteelandt, Stijn
作者单位：Ghent University; Ghent University
摘要：Over the past decade, doubly robust estimators have been proposed for a variety of target parameters in causal inference and missing data models. These are asymptotically unbiased when at least one of two nuisance working models is correctly specified, regardless of which. While their asymptotic distribution is not affected by the choice of root-n consistent estimators of the nuisance parameters indexing these working models when all working models are correctly specified, this choice of estim...
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作者：De Neve, Jan; Thas, Olivier
作者单位：Ghent University; University of Wollongong
摘要：We demonstrate how many classical rank tests, such as the Wilcoxon Mann Whitney, Kruskal Wallis, and Friedman test, can be embedded in a statistical modeling framework and how the method can be used to construct new rank tests. In addition to hypothesis testing, the method allows for estimating effect sizes with an informative interpretation, resulting in a better understanding of the data. Supplementary materials for this article are available online.
