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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....
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作者：Leng, Chenlei; Li, Bo
作者单位：National University of Singapore; Tsinghua University
摘要：We propose a simple forward adaptive banding method for estimating large covariance matrices using the modified Cholesky decomposition. This approach requires the fitting of a prespecified set of models due to the adaptive banding structure and can be efficiently implemented. Aside from its computational attractiveness, we propose a novel Bayes information criterion that gives consistent model selection for estimating high dimensional covariance matrices. The method compares favourably to its ...
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作者：Papathomas, M.; Dellaportas, P.; Vasdekis, V. G. S.
作者单位：Imperial College London; Athens University of Economics & Business
摘要：We propose a novel methodology to construct proposal densities in reversible jump algorithms that obtain samples from parameter subspaces of competing generalized linear models with differing dimensions. The derived proposal densities are not restricted to moves between nested models and are applicable even to models that share no common parameters. We illustrate our methodology on competing logistic regression and log-linear graphical models, demonstrating how our suggested proposal densities...
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作者：Neuhaus, John M.; McCulloch, Charles E.
作者单位：University of California System; University of California San Francisco
摘要：In standard regression analyses of clustered data, one typically assumes that the expected value of the response is independent of cluster size. However, this is often false. For example, in studies of surgical interventions, investigators have frequently found surgery volume and outcomes to be related to the skill level of the surgeons. This paper examines the effect of ignoring response-dependent, informative, cluster sizes on standard analytical methods such as mixed-effects models and cond...
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作者：Feng, Xingdong; He, Xuming; Hu, Jianhua
作者单位：Shanghai University of Finance & Economics; University of Michigan System; University of Michigan; University of Texas System; UTMD Anderson Cancer Center
摘要：The existing theory of the wild bootstrap has focused on linear estimators. In this note, we broaden its validity by providing a class of weight distributions that is asymptotically valid for quantile regression estimators. As most weight distributions in the literature lead to biased variance estimates for nonlinear estimators of linear regression, we propose a modification of the wild bootstrap that admits a broader class of weight distributions for quantile regression. A simulation study on...
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作者：Drton, Mathias; Williams, Benjamin
作者单位：University of Chicago; University of Chicago
摘要：When testing geometrically irregular parametric hypotheses, the bootstrap is an intuitively appealing method to circumvent difficult distribution theory. It has been shown, however, that the usual bootstrap is inconsistent in estimating the asymptotic distributions involved in such problems. This paper is concerned with the asymptotic size of likelihood ratio tests when critical values are computed using the inconsistent bootstrap. We clarify how the asymptotic size of such a test can be obtai...
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作者：De Luna, Xavier; Waernbaum, Ingeborg; Richardson, Thomas S.
作者单位：Umea University; University of Washington; University of Washington Seattle
摘要：Observational studies in which the effect of a nonrandomized treatment on an outcome of interest is estimated are common in domains such as labour economics and epidemiology. Such studies often rely on an assumption of unconfounded treatment when controlling for a given set of observed pre-treatment covariates. The choice of covariates to control in order to guarantee unconfoundedness should primarily be based on subject matter theories, although the latter typically give only partial guidance...
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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...
