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作者：Rosenblum, Michael
作者单位：Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
摘要：Prior work has shown that the power of adaptive designs with rules for modifying the sample size can always be matched or improved by suitably chosen, standard, group sequential designs. A natural question is whether analogous results hold for other types of adaptive designs. We focus on adaptive enrichment designs, which involve preplanned rules for modifying enrollment criteria based on accrued data in a randomized trial. Such designs often involve multiple hypotheses, e.g., one for the tota...
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作者：Chang, Jinyuan; Hall, Peter
作者单位：Southwestern University of Finance & Economics - China; University of Melbourne
摘要：We show that, when the double bootstrap is used to improve performance of bootstrap methods for bias correction, techniques based on using a single double-bootstrap sample for each single-bootstrap sample can produce third-order accuracy for much less computational expense than is required by conventional double-bootstrap methods. However, this improved level of performance is not available for the single double-bootstrap methods that have been suggested to construct confidence intervals or di...
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作者：Crane, Harry
作者单位：Rutgers University System; Rutgers University New Brunswick
摘要：We propose a Bayesian method for clustering from discrete data structures that commonly arise in genetics and other applications. This method is equivariant with respect to relabelling units; unsampled units do not interfere with sampled data; and missing data do not hinder inference. Cluster inference using the posterior mode performs well on simulated and real datasets, and the posterior predictive distribution enables supervised learning based on a partial clustering of the sample.
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作者：Belloni, A.; Chernozhukov, V.; Kato, K.
作者单位：Duke University; Massachusetts Institute of Technology (MIT); University of Tokyo
摘要：We develop uniformly valid confidence regions for regression coefficients in a high-dimensional sparse median regression model with homoscedastic errors. Our methods are based on a moment equation that is immunized against nonregular estimation of the nuisance part of the median regression function by using Neyman's orthogonalization. We establish that the resulting instrumental median regression estimator of a target regression coefficient is asymptotically normally distributed uniformly with...
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作者：Luo, Xiaodong; Tsai, Wei Yann
作者单位：Icahn School of Medicine at Mount Sinai; Columbia University
摘要：Luo & Tsai, Biometrika 99, 211-22, 2012, proposed a proportional likelihood ratio model and discussed a maximum likelihood method for its parameter estimation. In this paper, we use this model as the marginal distribution to analyse longitudinal data, where the maximum likelihood method is not directly applicable because the joint distribution is not fully specified. We propose a moment-type method that is an extension of the generalized estimating equation method. The resulting estimators are...
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作者：Chen, Shizhe; Witten, Daniela M.; Shojaie, Ali
作者单位：University of Washington; University of Washington Seattle
摘要：We consider the problem of estimating the parameters in a pairwise graphical model in which the distribution of each node, conditioned on the others, may have a different exponential family form. We identify restrictions on the parameter space required for the existence of a well-defined joint density, and establish the consistency of the neighbourhood selection approach for graph reconstruction in high dimensions when the true underlying graph is sparse. Motivated by our theoretical results, ...
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作者：Zhang, Xiaoke; Wang, Jane-Ling
作者单位：University of Delaware; University of California System; University of California Davis
摘要：Both varying-coefficient and additive models have been studied extensively in the literature as extensions to linear models. They have also been extended to deal with functional response data. However, existing extensions are still not flexible enough to reflect the functional nature of the responses. In this paper, we extend varying-coefficient and additive models to obtain a much more flexible model and propose a simple algorithm to estimate its nonparametric additive and varying-coefficient...
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作者：Shao, J.; Zhang, J.
作者单位：East China Normal University; University of Wisconsin System; University of Wisconsin Madison
摘要：We consider a linear mixed-effects model in which the response panel vector has missing components and the missing data mechanism depends on observed data as well as missing responses through unobserved random effects. Using a transformation of the data that eliminates the random effects, we derive asymptotically unbiased and normally distributed estimators of certain model parameters. Estimators of model parameters that cannot be estimated using the transformed data are also constructed, and ...
