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作者：Mai, Qing; Zou, Hui; Yuan, Ming
作者单位：University of Minnesota System; University of Minnesota Twin Cities; University System of Georgia; Georgia Institute of Technology
摘要：Sparse discriminant methods based on independence rules, such as the nearest shrunken centroids classifier (Tibshirani et al., 2002) and features annealed independence rules (Fan & Fan, 2008), have been proposed as computationally attractive tools for feature selection and classification with high-dimensional data. A fundamental drawback of these rules is that they ignore correlations among features and thus could produce misleading feature selection and inferior classification. We propose a n...
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作者：Mukerjee, Rahul; Tang, Boxin
作者单位：Indian Institute of Management (IIM System); Indian Institute of Management Calcutta; Simon Fraser University
摘要：Two-level fractional factorial designs are considered under a baseline parameterization. The criterion of minimum aberration is formulated in this context and optimal designs under this criterion are investigated. The underlying theory and the concept of isomorphism turn out to be significantly different from their counterparts under orthogonal parameterization, and this is reflected in the optimal designs obtained.
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作者：Yi, Grace Y.; Ma, Yanyuan; Carroll, Raymond J.
作者单位：University of Waterloo; Texas A&M University System; Texas A&M University College Station
摘要：Covariate measurement error and missing responses are typical features in longitudinal data analysis. There has been extensive research on either covariate measurement error or missing responses, but relatively little work has been done to address both simultaneously. In this paper, we propose a simple method for the marginal analysis of longitudinal data with time-varying covariates, some of which are measured with error, while the response is subject to missingness. Our method has a number o...
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作者：Zhou, Ming; Kim, Jae Kwang
作者单位：Iowa State University
摘要：Panel attrition is frequently encountered in panel sample surveys. When it is related to the observed study variable, the classical approach of nonresponse adjustment using a covariate-dependent dropout mechanism can be biased. We consider an efficient method of estimation with monotone panel attrition when the response probability depends on the previous values of study variable as well as other covariates. Because of the monotone structure of the missing pattern, the response mechanism is mi...
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作者：Lu, W.; Goldberg, Y.; Fine, J. P.
作者单位：North Carolina State University; University of North Carolina; University of North Carolina Chapel Hill
摘要：Penalization methods have been shown to yield both consistent variable selection and oracle parameter estimation under correct model specification. In this article, we study such methods under model misspecification, where the assumed form of the regression function is incorrect, including generalized linear models for uncensored outcomes and the proportional hazards model for censored responses. Estimation with the adaptive least absolute shrinkage and selection operator, lasso, penalty is pr...
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作者：Leng, Chenlei; Tang, Cheng Yong
作者单位：National University of Singapore
摘要：When a parametric likelihood function is not specified for a model, estimating equations may provide an instrument for statistical inference. Qin and Lawless (1994) illustrated that empirical likelihood makes optimal use of these equations in inferences for fixed low-dimensional unknown parameters. In this paper, we study empirical likelihood for general estimating equations with growing high dimensionality and propose a penalized empirical likelihood approach for parameter estimation and vari...
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作者：Wang, Fei; Wang, Lu; Song, Peter X. -K.
作者单位：University of Michigan System; University of Michigan
摘要：Merging data from multiple studies has been widely adopted in biomedical research. In this paper, we consider two major issues related to merging longitudinal datasets. We first develop a rigorous hypothesis testing procedure to assess the validity of data merging, and then propose a flexible joint estimation procedure that enables us to analyse merged data and to account for different within-subject correlations and follow-up schedules in different studies. We establish large sample propertie...
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作者：Lekivetz, Ryan; Tang, Boxin
作者单位：Simon Fraser University
摘要：Orthogonal arrays with clear two-factor interactions provide a class of designs that are robust to nonnegligible effects. If certain prior knowledge is available, then robust designs allow additional factors to be studied. This is done through partially clear two-factor interactions. We study the existence and construction of such robust designs and present an upper bound on the maximum number of clear two-factor interactions.
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作者：Mallik, A.; Sen, B.; Banerjee, M.; Michailidis, G.
作者单位：University of Michigan System; University of Michigan; Columbia University
摘要：We use p-values to identify the threshold level at which a regression function leaves its baseline value, a problem motivated by applications in toxicological and pharmacological dose-response studies and environmental statistics. We study the problem in two sampling settings: one where multiple responses can be obtained at a number of different covariate levels, and the other the standard regression setting involving limited number of response values at each covariate. Our procedure involves ...
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作者：Belloni, A.; Chernozhukov, V.; Wang, L.
作者单位：Duke University; Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT)
摘要：We propose a pivotal method for estimating high-dimensional sparse linear regression models, where the overall number of regressors p is large, possibly much larger than n, but only s regressors are significant. The method is a modification of the lasso, called the square-root lasso. The method is pivotal in that it neither relies on the knowledge of the standard deviation Sigma nor does it need to pre-estimate Sigma. Moreover, the method does not rely on normality or sub-Gaussianity of noise....
