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作者：Donohue, M. C.; Overholser, R.; Xu, R.; Vaida, F.
作者单位：University of California System; University of California San Diego; University of California System; University of California San Diego
摘要：We study model selection for clustered data, when the focus is on cluster specific inference. Such data are often modelled using random effects, and conditional Akaike information was proposed in Vaida & Blanchard (2005) and used to derive an information criterion under linear mixed models. Here we extend the approach to generalized linear and proportional hazards mixed models. Outside the normal linear mixed models, exact calculations are not available and we resort to asymptotic approximatio...
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作者：Ballani, F.; Schlather, M.
作者单位：Technical University Freiberg; University of Gottingen
摘要：We present a construction principle for the spectral density of a multivariate extreme value distribution. It generalizes the pairwise beta model introduced in the literature recently and may be used to obtain new parametric models from lower dimensional spectral densities. We illustrate the flexibility of this new class of models and apply it to a wind speed dataset.
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作者：Pati, D.; Reich, B. J.; Dunson, D. B.
作者单位：Duke University; North Carolina State University
摘要：We consider geostatistical models that allow the locations at which data are collected to be informative about the outcomes. A Bayesian approach is proposed, which models the locations using a log Gaussian Cox process, while modelling the outcomes conditionally on the locations as Gaussian with a Gaussian process spatial random effect and adjustment for the location intensity process. We prove posterior propriety under an improper prior on the parameter controlling the degree of informative sa...
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作者：Chaudhuri, Sanjay; Ghosh, Malay
作者单位：National University of Singapore; State University System of Florida; University of Florida
摘要：Current methodologies in small area estimation are mostly either parametric or heavily dependent on the assumed linearity of the estimators of the small area means. We discuss an alternative empirical likelihood-based Bayesian approach, which neither requires a parametric likelihood nor assumes linearity of the estimators, and can handle both discrete and continuous data in a unified manner. Empirical likelihoods for both area- and unit-level models are introduced. We discuss the suitability o...
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作者：Furrer, Reinhard; Genton, Marc G.
作者单位：University of Zurich; Texas A&M University System; Texas A&M University College Station
摘要：Best linear unbiased prediction of spatially correlated multivariate random processes, often called cokriging in geostatistics, requires the solution of a large linear system based on the covariance and cross-covariance matrix of the observations. For many problems of practical interest, it is impossible to solve the linear system with direct methods. We propose an efficient linear unbiased predictor based on a linear aggregation of the covariables. The primary variable together with this sing...
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作者：Martin, Ryan; Tokdar, Surya T.
作者单位：Purdue University System; Purdue University; Purdue University in Indianapolis; Duke University
摘要：Predictive recursion is an accurate and computationally efficient algorithm for nonparametric estimation of mixing densities in mixture models. In semiparametric mixture models, however, the algorithm fails to account for any uncertainty in the additional unknown structural parameter. As an alternative to existing profile likelihood methods, we treat predictive recursion as a filter approximation by fitting a fully Bayes model, whereby an approximate marginal likelihood of the structural param...
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作者：Tan, Z.
作者单位：Rutgers University System; Rutgers University New Brunswick
摘要：Consider a conditional mean model with missing data on the response or explanatory variables due to two-phase sampling or nonresponse. Robins et al. (1994) introduced a class of augmented inverse-probability-weighted estimators, depending on a vector of functions of explanatory variables and a vector of functions of coarsened data. Tsiatis (2006) studied two classes of restricted estimators, class 1 with both vectors restricted to finite-dimensional linear subspaces and class 2 with the first ...
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作者：Bhattacharya, A.; Dunson, D. B.
作者单位：Duke University
摘要：We focus on sparse modelling of high-dimensional covariance matrices using Bayesian latent factor models. We propose a multiplicative gamma process shrinkage prior on the factor loadings which allows introduction of infinitely many factors, with the loadings increasingly shrunk towards zero as the column index increases. We use our prior on a parameter-expanded loading matrix to avoid the order dependence typical in factor analysis models and develop an efficient Gibbs sampler that scales well...
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作者：Poyiadjis, George; Doucet, Arnaud; Singh, Sumeetpal S.
作者单位：University of British Columbia; University of Cambridge
摘要：Particle methods are popular computational tools for Bayesian inference in nonlinear non-Gaussian state space models. For this class of models, we present two particle algorithms to compute the score vector and observed information matrix recursively. The first algorithm is implemented with computational complexity O(N) and the second with complexity O(N-2), where N is the number of particles. Although cheaper, the performance of the O(N) method degrades quickly, as it relies on the approximat...
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作者：Siegmund, D. O.; Zhang, N. R.; Yakir, B.
作者单位：Stanford University; Hebrew University of Jerusalem
摘要：The false discovery rate is a criterion for controlling Type I error in simultaneous testing of multiple hypotheses. For scanning statistics, due to local dependence, clusters of neighbouring hypotheses are likely to be rejected together. In such situations, it is more intuitive and informative to group neighbouring rejections together and count them as a single discovery, with the false discovery rate defined as the proportion of clusters that are falsely declared among all declared clusters....
