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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...
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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：Crane, Harry
作者单位：Rutgers University System; Rutgers University New Brunswick
摘要：The three-parameter cluster model is a combinatorial stochastic process that generates categorical response sequences by randomly perturbing a fixed clustering parameter. This clear relationship between the observed data and the underlying clustering is particularly attractive in cluster analysis, in which supervised learning is a common goal and missing data is a familiar issue. The model is well equipped for this task, as it can handle missing data, perform out-of-sample inference, and accom...
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作者：Gregory, Karl Bruce; Carroll, Raymond J.; Baladandayuthapani, Veerabhadran; Lahiri, Soumendra N.
作者单位：Texas A&M University System; Texas A&M University College Station; University of Texas System; UTMD Anderson Cancer Center; North Carolina State University
摘要：We develop a test statistic for testing the equality of two population mean vectors in the large-p-small-n setting. Such a test must surmount the rank-deficiency of the sample covariance matrix, which breaks down the classic Hotel ling T-2 test. The proposed procedure, called the generalized component test, avoids full estimation of the covariance matrix by assuming that the p components admit a logical ordering such that the dependence between components is related to their displacement. The ...
