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作者：Lemieux, Thomas
作者单位：University of British Columbia
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作者：McCormick, Tyler H.; Zheng, Tian
作者单位：University of Washington; University of Washington Seattle; Columbia University
摘要：Despite increased interest across a range of scientific applications in modeling and understanding social network structure, collecting complete network data remains logistically and financially challenging, especially in the social sciences. This article introduces a latent surface representation of social network structure for partially observed network data. We derive a multivariate measure of expected (latent) distance between an observed actor and unobserved actors with given features. We...
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作者：Zhao, Jiwei; Shao, Jun
作者单位：State University of New York (SUNY) System; University at Buffalo, SUNY; East China Normal University; University of Wisconsin System; University of Wisconsin Madison
摘要：We consider identifiability and estimation in a generalized linear model in which the response variable and some covariates have missing values and the missing data mechanism is nonignorable and unspecified. We adopt a pseudo-likelihood approach that makes use of an instrumental variable to help identifying unknown parameters in the presence of nonignorable missing data. Explicit conditions for the identifiability of parameters are given. Some asymptotic properties of the parameter estimators ...
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作者：Yi, Grace Y.; Ma, Yanyuan; Spiegelman, Donna; Carroll, Raymond J.
作者单位：University of Waterloo; Texas A&M University System; Texas A&M University College Station; Harvard University; Harvard T.H. Chan School of Public Health; Texas A&M University System; Texas A&M University College Station; University of Technology Sydney
摘要：Covariate measurement imprecision or errors arise frequently in many areas. It is well known that ignoring such errors can substantially degrade the quality of inference or even yield erroneous results. Although in practice both covariates subject to measurement error and covariates subject to misclassification can occur, research attention in the literature has mainly focused on addressing either one of these problems separately. To fill this gap, we develop estimation and inference methods t...
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作者：Fukumoto, Kentaro
作者单位：Gakushuin University
摘要：Scholars are interested in not just what event happens but also when the event happens. If there is dependence among events or dependence between time and events, however, the currently common methods (e.g., competing risks approaches) produce biased estimates. To deal with these problems, this article proposes a new method of copula-based ordered event history analysis (COEHA). A merit of working with copulas is that, whatever marginal distributions time and event variables follow (including ...
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作者：Hui, Francis K. C.; Warton, David I.; Foster, Scott D.
作者单位：University of New South Wales Sydney; Commonwealth Scientific & Industrial Research Organisation (CSIRO); Commonwealth Scientific & Industrial Research Organisation (CSIRO)
摘要：The adaptive Lasso is a commonly applied penalty for variable selection in regression modeling. Like all penalties though, its performance depends critically on the choice of the tuning parameter. One method for choosing the tuning parameter is via information criteria, such as those based on AIC and BIC. However, these criteria were developed for use with unpenalized maximum likelihood estimators, and it is not clear that they take into account the effects of penalization. In this article, we...
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作者：Shao, Xiaofeng
作者单位：University of Illinois System; University of Illinois Urbana-Champaign
摘要：This article reviews some recent developments on the inference of time series data using the self-normalized approach. We aim to provide a detailed discussion about the use of self-normalization in different contexts and highlight distinctive feature associated with each problem and connections among these recent developments. The topics covered include: confidence interval construction for a parameter in a weakly dependent stationary time series setting, change point detection in the mean, ro...
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作者：Wang, Weizhen
作者单位：Beijing University of Technology; University System of Ohio; Wright State University Dayton
摘要：For a hypergeometric distribution, denoted by Hyper(M, N, n), where N is the population size, M is the number of population units with some attribute, and n is the given sample size, there are two parametric cases: (i) N is unknown and M is given; (ii) M is unknown and N is given. For each case, we first show that the minimum coverage probability of commonly used approximate intervals is much smaller than the nominal level for any n, then we provide exact smallest lower and upper one-sided con...
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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...
