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作者：Atkinson, A. C.
作者单位：University of London; London School Economics & Political Science
摘要：Optimum designs are described for two treatments with different variances when covariates are included in the model. The designs, a generalization of Neyman allocation, are required in personalized medicine to model the effect of covariates on the choice of treatment. The use of the designs in clinical trials is indicated. D-optimality of the designs is established using results from Kiefer's general equivalence theorem. The results are obtained with the use of surprisingly elementary algebra.
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作者：Zeng, D.; Lin, D. Y.
作者单位：University of North Carolina; University of North Carolina Chapel Hill
摘要：Meta-analysis is widely used to compare and combine the results of multiple independent studies. To account for between-study heterogeneity, investigators often employ random-effects models, under which the effect sizes of interest are assumed to follow a normal distribution. It is common to estimate the mean effect size by a weighted linear combination of study-specific estimators, with the weight for each study being inversely proportional to the sum of the variance of the effect-size estima...
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作者：Yu, Y.; Wang, T.; Samworth, R. J.
作者单位：University of Cambridge
摘要：The Davis-Kahan theorem is used in the analysis of many statistical procedures to bound the distance between subspaces spanned by population eigenvectors and their sample versions. It relies on an eigenvalue separation condition between certain population and sample eigenvalues. We present a variant of this result that depends only on a population eigenvalue separation condition, making it more natural and convenient for direct application in statistical contexts, and provide an improvement in...
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作者：Janson, Lucas; Fithian, William; Hastie, Trevor J.
作者单位：Stanford University
摘要：To most applied statisticians, a fitting procedure's degrees of freedom is synonymous with its model complexity, or its capacity for overfitting to data. In particular, the degrees of freedom is often used to parameterize the bias-variance trade-off in model selection. We argue that, on the contrary, model complexity and degrees of freedom may correspond very poorly. We exhibit and theoretically explore various fitting procedures for which the degrees of freedom is not monotonic in the model c...
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作者：Doucet, A.; Pitt, M. K.; Deligiannidis, G.; Kohn, R.
作者单位：University of Oxford; University of Warwick; University of New South Wales Sydney
摘要：When an unbiased estimator of the likelihood is used within a Metropolis-Hastings chain, it is necessary to trade off the number of Monte Carlo samples used to construct this estimator against the asymptotic variances of the averages computed under this chain. Using many Monte Carlo samples will typically result in Metropolis-Hastings averages with lower asymptotic variances than the corresponding averages that use fewer samples; however, the computing time required to construct the likelihood...
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作者：Ma, Yanyuan; Zhang, Xinyu
作者单位：University of South Carolina System; University of South Carolina Columbia; Capital University of Economics & Business
摘要：A crucial component of performing sufficient dimension reduction is to determine the structural dimension of the reduction model. We propose a novel information criterion-based method for this purpose, a special feature of which is that when examining the goodness-of-fit of the current model, one needs to perform model evaluation by using an enlarged candidate model. Although the procedure does not require estimation under the enlarged model of dimension k + 1, the decision as to how well the ...
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作者：Xia, Yin; Cai, Tianxi; Cai, T. Tony
作者单位：University of North Carolina; University of North Carolina Chapel Hill; Harvard University; Harvard T.H. Chan School of Public Health; University of Pennsylvania
摘要：Model organisms and human studies have yielded increasing empirical evidence that interactions among genes contribute broadly to genetic variation of complex traits. In the presence of gene-gene interactions, the dimensionality of the feature space becomes extremely high relative to the sample size. This poses a significant methodological challenge in the identification of gene-gene interactions. In this paper, by using a Gaussian graphical model framework, we translate the problem of identify...
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作者：Wu, Yichao; Stefanski, Leonard A.
作者单位：North Carolina State University
摘要：We propose an automatic structure recovery method for additive models, based on a backfitting algorithm coupled with local polynomial smoothing, in conjunction with a new kernel-based variable selection strategy. Our method produces estimates of the set of noise predictors, the sets of predictors that contribute polynomially at different degrees up to a specified degree M, and the set of predictors that contribute beyond polynomially of degree M. We prove consistency of the proposed method, an...
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作者：Joseph, V. Roshan; Gul, Evren; Ba, Shan
作者单位：University System of Georgia; Georgia Institute of Technology; Procter & Gamble
摘要：Space-filling properties are important in designing computer experiments. The traditional maximin and minimax distance designs consider only space-filling in the full-dimensional space; this can result in poor projections onto lower-dimensional spaces, which is undesirable when only a few factors are active. Restricting maximin distance design to the class of Latin hypercubes can improve one-dimensional projections but cannot guarantee good space-filling properties in larger subspaces. We prop...
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作者：Xia, Zhiming; Qiu, Peihua
作者单位：Northwest University Xi'an; State University System of Florida; University of Florida
摘要：Nonparametric regression analysis when the regression function is discontinuous has many applications. Existing methods for estimating a discontinuous regression curve usually assume that the number of jumps in the regression curve is known beforehand, which is unrealistic in some situations. Although there has been research on estimation of a discontinuous regression curve when the number of jumps is unknown, the problem remains mostly open because such research often requires assumptions on ...
