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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：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：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：Samia, Noelle I.; Chan, Kung-Sik
作者单位：Northwestern University; University of Iowa
摘要：There is hardly any literature on modelling nonlinear dynamic relations involving nonnormal time series data. This is a serious lacuna because nonnormal data are far more abundant than normal ones, for example, time series of counts and positive time series. While there are various forms of nonlinearities, the class of piecewise-linear models is particularly appealing for its relative ease of tractability and interpretation. We propose to study the generalized threshold model which specifies t...
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作者：Zhu, Hongtu; Ibrahim, Joseph G.; Tang, Niansheng
作者单位：University of North Carolina; University of North Carolina Chapel Hill; Yunnan University
摘要：In this paper we develop a general framework of Bayesian influence analysis for assessing various perturbation schemes to the data, the prior and the sampling distribution for a class of statistical models. We introduce a perturbation model to characterize these various perturbation schemes. We develop a geometric framework, called the Bayesian perturbation manifold, and use its associated geometric quantities including the metric tensor and geodesic to characterize the intrinsic structure of ...
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作者：Xie, Jichun; Cai, T. Tony; Li, Hongzhe
作者单位：University of Pennsylvania; University of Pennsylvania
摘要：Genome-wide association studies have successfully identified hundreds of novel genetic variants associated with many complex human diseases. However, there is a lack of rigorous work on evaluating the statistical power for identifying these variants. In this paper, we consider sparse signal identification in genome-wide association studies and present two analytical frameworks for detailed analysis of the statistical power for detecting and identifying the disease-associated variants. We prese...
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作者：Levine, M.; Hunter, D. R.; Chauveau, D.
作者单位：Purdue University System; Purdue University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Universite de Orleans
摘要：We introduce an algorithm for estimating the parameters in a finite mixture of completely unspecified multivariate components in at least three dimensions under the assumption of conditionally independent coordinate dimensions. We prove that this algorithm, based on a majorization-minimization idea, possesses a desirable descent property just as any em algorithm does. We discuss the similarities between our algorithm and a related one, the so-called nonlinearly smoothed em algorithm for the no...
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作者：Genton, Marc G.; Ma, Yanyuan; Sang, Huiyan
作者单位：Texas A&M University System; Texas A&M University College Station
摘要：We derive a closed form expression for the likelihood function of a Gaussian max-stable process indexed by R-d at p < d+1 sites, d >= 1. We demonstrate the gain in efficiency in the maximum composite likelihood estimators of the covariance matrix from p=2 to p=3 sites in R-2 by means of a Monte Carlo simulation study.
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作者：Efron, Bradley; Zhang, Nancy R.
作者单位：Stanford University
摘要：Copy number changes, the gains and losses of chromosome segments, are a common type of genetic variation among healthy individuals as well as an important feature in tumour genomes. Microarray technology enables us to simultaneously measure, with moderate accuracy, copy number variation at more than a million chromosome locations and for hundreds of subjects. This leads to massive data sets and complicated inference problems concerning which locations are more likely to vary. In this paper we ...
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作者：Chauvet, G.; Deville, J. -C.; Haziza, D.
作者单位：Ecole Nationale de la Statistique et de l'Analyse de l'Information (ENSAI); Universite de Montreal
摘要：Random imputation methods are often used in practice because they tend to preserve the distribution of the variable being imputed, which is an important property when the goal is to estimate population quantiles. However, this type of imputation method introduces additional variability, the imputation variance, due to the random selection of residuals. In this paper, we propose a class of random balanced imputation methods under which the imputation variance is eliminated while the distributio...
