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作者：Chen, Ying Qing; Hu, Nan; Cheng, Su-Chun; Musoke, Philippa; Zhao, Lue Ping
作者单位：Utah System of Higher Education; University of Utah; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute; Makerere University
摘要：The proportional odds model may serve as a useful alternative to the Cox proportional hazards model to study association between covariates and their survival functions in medical studies. In this article, we study an extended proportional odds model that incorporates the so-called external time-varying covariates. In the extended model, regression parameters have a direct interpretation of comparing survival functions, without specifying the baseline survival odds function. Semiparametric and...
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作者：Wang, Lan; Wu, Yichao; Li, Runze
作者单位：University of Minnesota System; University of Minnesota Twin Cities; North Carolina State University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：Ultra-high dimensional data often display heterogeneity due to either heteroscedastic variance or other forms of non-location-scale covariate effects. To accommodate heterogeneity, we advocate a more general interpretation of sparsity, which assumes that only a small number of covariates influence the conditional distribution of the response variable, given all candidate covariates; however, the sets of relevant covariates may differ when we consider different segments of the conditional distr...
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作者：Bornn, Luke; Shaddick, Gavin; Zidek, James V.
作者单位：University of British Columbia; University of Bath
摘要：In this article, we propose a novel approach to modeling nonstationary spatial fields. The proposed method works by expanding the geographic plane over which these processes evolve into higher-dimensional spaces, transforming and clarifying complex patterns in the physical plane. By combining aspects of multidimensional scaling, group lasso, and latent variable models, a dimensionally sparse projection is found in which the originally nonstationary field exhibits stationarity. Following a comp...
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作者：Ma, Yanyuan; Zhu, Liping
作者单位：Texas A&M University System; Texas A&M University College Station; Shanghai University of Finance & Economics
摘要：We provide a novel and completely different approach to dimension-reduction problems from the existing literature. We cast the dimension-reduction problem in a semiparametric estimation framework and derive estimating equations. Viewing this problem from the new angle allows us to derive a rich class of estimators, and obtain the classical dimension reduction techniques as special cases in this class. The semiparametric approach also reveals that in the inverse regression context while keeping...
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作者：Sen, Bodhisattva; Chaudhuri, Probal
作者单位：Columbia University; Indian Statistical Institute; Indian Statistical Institute Kolkata
摘要：The need for comparing two regression functions arises frequently in statistical applications. Comparison of the usual regression functions is not very meaningful in situations where the distributions and the ranges of the covariates are different for the populations. For instance, in econometric studies, the prices of commodities and people's incomes observed at different time points may not be on comparable scales due to inflation and other economic factors. In this article, we describe a me...
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作者：Apanasovich, Tatiyana V.; Genton, Marc G.; Sun, Ying
作者单位：Thomas Jefferson University; Texas A&M University System; Texas A&M University College Station
摘要：We introduce a valid parametric family of cross-covariance functions for multivariate spatial random fields where each component has a covariance function from a well-celebrated Matern class. Unlike previous attempts, our model indeed allows for various smoothnesses and rates of correlation decay for any number of vector components. We present the conditions on the parameter space that result in valid models with varying degrees of complexity. We discuss practical implementations, including re...
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作者：Dupuis, Debbie J.
作者单位：Universite de Montreal; HEC Montreal
摘要：Heat waves are a serious threat to society, the environment, and the economy. Estimates of the recurrence probabilities of heat waves may be obtained following the successful modeling of daily maximum temperature, but working with the latter is difficult as we have to recognize, and allow for, not only a time trend but also seasonality in the mean and in the variability, as well as serial correlation. Furthermore, as the extreme values of daily maximum temperature have a different form of nons...
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作者：Papp, David
作者单位：Northwestern University
摘要：We consider consider the problem of finding optimal nonsequential designs for a large class of regression models involving polynomials and rational functions with heteroscedastic noise also given by a polynomial or rational weight function. Since the design weights can be found easily by existing methods once the support is known, we concentrate on determining the support of the optimal design. The proposed method treats D-, E-, A-, and Phi(p)-optimal designs in a unified manner, and generates...
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作者：Hu, Zonghui; Follmann, Dean A.; Qin, Jing
作者单位：National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID)
摘要：When estimating the marginal mean response with missing observations, a critical issue is robustness to model misspecification. In this article, we propose a semiparametric estimation method with extended double robustness that attains the optimal efficiency under less stringent requirement for model specifications than the doubly robust estimators. In this semiparametric estimation, covariate information is collapsed into a two-dimensional score S. with one dimension for (i) the pattern of mi...
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作者：Desai, Keyur H.; Storey, John D.
作者单位：Princeton University; Princeton University
摘要：A growing number of modern scientific problems in areas such as genomics, neurobiology, and spatial epidemiology involve the measurement and analysis of thousands of related features that may be stochastically dependent at arbitrarily strong levels. In this work, we consider The scenario where the features follow a multivariate Normal distribution. We demonstrate that dependence is manifested as random variation shared among features, and that standard methods may yield highly unstable inferen...
