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作者：Papaspiliopoulos, O.; Rossell, D.
作者单位：ICREA; Pompeu Fabra University
摘要：We propose a scalable algorithmic framework for exact Bayesian variable selection and model averaging in linear models under the assumption that the Gram matrix is block-diagonal, and as a heuristic for exploring the model space for general designs. In block-diagonal designs our approach returns the most probable model of any given size without resorting to numerical integration. The algorithm also provides a novel and efficient solution to the frequentist best subset selection problem for blo...
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作者：Stein, M. L.
作者单位：University of Chicago
摘要：Motivated by the study of annual temperature extremes, two new results on the limiting distribution of block maxima of random variables with varying upper bounds are obtained. One gives a generalized extreme value distribution as the limit, but with a different shape parameter from that obtained when the bound on the random variables does not vary. The other gives a limiting distribution that is only a generalized extreme value in certain cases. Both results consider triangular arrays of rando...
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作者：Xiao, Qian; Xu, Hongquan
作者单位：University of California System; University of California Los Angeles
摘要：Maximin distance Latin hypercube designs are widely used in computer experiments, yet their construction is challenging. Based on number theory and finite fields, we propose three algebraic methods to construct maximin distance Latin squares as special Latin hypercube designs. We develop lower bounds on their minimum distances. The resulting Latin squares and related Latin hypercube designs have larger minimum distances than existing ones, and are especially appealing for high-dimensional appl...
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作者：Kim, J. K.; Yang, S.
作者单位：Iowa State University; North Carolina State University
摘要：Multiple imputation is popular for handling item nonresponse in survey sampling. Current multiple imputation techniques with complex survey data assume that the sampling design is ignorable. In this paper, we propose a new multiple imputation procedure for parametric inference without this assumption. Instead of using the sample-data likelihood, we use the sampling distribution of the pseudo maximum likelihood estimator to derive the posterior distribution of the parameters. The asymptotic pro...
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作者：Huang, Ming-Yueh; Chan, Kwun Chuen Gary
作者单位：University of Washington; University of Washington Seattle
摘要：The estimation of treatment effects based on observational data usually involves multiple confounders, and dimension reduction is often desirable and sometimes inevitable. We first clarify the definition of a central subspace that is relevant for the efficient estimation of average treatment effects. A criterion is then proposed to simultaneously estimate the structural dimension, the basis matrix of the joint central subspace, and the optimal bandwidth for estimating the conditional treatment...
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作者：Molina, J.; Rotnitzky, A.; Sued, M.; Robins, J. M.
作者单位：University of Buenos Aires; Universidad Torcuato Di Tella; Harvard University; Harvard T.H. Chan School of Public Health
摘要：We consider inference under a nonparametric or semiparametric model with likelihood that factorizes as the product of two or more variation-independent factors. We are interested in a finitedimensional parameter that depends on only one of the likelihood factors and whose estimation requires the auxiliary estimation of one or several nuisance functions. We investigate general structures conducive to the construction of so-called multiply robust estimating functions, whose computation requires ...
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作者：Wang, Linbo; Zhou, Xiao-Hua; Richardson, Thomas S.
作者单位：Harvard University; Harvard T.H. Chan School of Public Health; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
摘要：It is common in medical studies that the outcome of interest is truncated by death, meaning that a subject has died before the outcome could be measured. In this case, restricted analysis among survivors may be subject to selection bias. Hence, it is of interest to estimate the survivor average causal effect, defined as the average causal effect among the subgroup consisting of subjects who would survive under either exposure. In this paper, we consider the identification and estimation proble...
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作者：Lee, Seunggeun; Sun, Wei; Wright, Fred A.; Zou, Fei
作者单位：University of Michigan System; University of Michigan; Fred Hutchinson Cancer Center; North Carolina State University; State University System of Florida; University of Florida
摘要：Unobserved environmental, demographic and technical factors can adversely affect the estimation and testing of the effects of primary variables. Surrogate variable analysis, proposed to tackle this problem, has been widely used in genomic studies. To estimate hidden factors that are correlated with the primary variables, surrogate variable analysis performs principal component analysis either on a subset of features or on all features, but weighting each differently. However, existing approach...
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作者：Dai, Xiongtao; Mueller, Hans-Georg; Yao, Fang
作者单位：University of California System; University of California Davis; Peking University; Peking University
摘要：Bayes classifiers for functional data pose a challenge. One difficulty is that probability density functions do not exist for functional data, so the classical Bayes classifier using density quotients needs to be modified. We propose to use density ratios of projections onto a sequence of eigenfunctions that are common to the groups to be classified. The density ratios are then factorized into density ratios of individual projection scores, reducing the classification problem to obtaining a se...
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作者：Jadhav, S.; Koul, H. L.; Lu, Q.
作者单位：Michigan State University; Michigan State University
摘要：This paper considers testing for no effect of functional covariates on response variables in multivariate regression. We use generalized estimating equations to determine the underlying parameters and establish their joint asymptotic normality. This is then used to test the significance of the effect of predictors on the vector of response variables. Simulations demonstrate the importance of considering existing correlation structures in the data. To explore the effect of treating genetic data...
