4期

最新文章

• Multivariate spatial covariance models: a conditional approach AI翻译
  作者：Cressie, Noel; Zammit-Mangion, Andrew
  作者单位：University of Wollongong
  摘要：Multivariate geostatistics is based on modelling all covariances between all possible combinations of two or more variables at any sets of locations in a continuously indexed domain. Multivariate spatial covariance models need to be built with care, since any covariance matrix that is derived from such a model must be nonnegative-definite. In this article, we develop a conditional approach for spatial-model construction whose validity conditions are easy to check. We start with bivariate spati...
• Nonnested model comparisons for time series AI翻译
  作者：Mcelroy, T. S.
  摘要：This paper addresses the topic of nonnested time series model comparisons. The main result is a central limit theorem for the likelihood ratio statistic when the models are nonnested and non-equivalent. The concepts of model equivalence and forecast equivalence, which are important for determining the parameter subset corresponding to the null hypothesis, are developed. The method is validated through a simulation study and illustrated on a retail time series.
• Replicates in high dimensions, with applications to latent variable graphical models AI翻译
  作者：Tan, Kean Ming; Ning, Yang; Witten, Daniela M.; Liu, Han
  摘要：In classical statistics, much thought has been put into experimental design and data collection. In the high-dimensional setting, however, experimental design has been less of a focus. In this paper, we stress the importance of collecting multiple replicates for each subject in the high-dimensional setting. We consider learning the structure of a graphical model with latent variables, under the assumption that these variables take a constant value across replicates within each subject. By coll...
• Misclassified group-tested current status data AI翻译
  作者：Petito, L. C.; Jewell, N. P.
  作者单位：University of California System; University of California Berkeley
  摘要：Group testing, introduced by Dorfman (1943), has been used to reduce costs when estimating the prevalence of a binary characteristic based on a screening test of k groups that include n independent individuals in total. If the unknown prevalence is low and the screening test suffers from misclassification, it is also possible to obtain more precise prevalence estimates than those obtained from testing all n samples separately (Tu et al., 1994). In some applications, the individual binary respo...
• Bayesian inference on quasi-sparse count data AI翻译
  作者：Datta, Jyotishka; Dunson, David B.
  作者单位：University of Arkansas System; University of Arkansas Fayetteville; Duke University
  摘要：There is growing interest in analysing high-dimensional count data, which often exhibit quasi-sparsity corresponding to an overabundance of zeros and small nonzero counts. Existing methods for analysing multivariate count data via Poisson or negative binomial log-linear hierarchical models with zero-inflation cannot flexibly adapt to quasi-sparse settings. We develop a new class of continuous local-global shrinkage priors tailored to quasi-sparse counts. Theoretical properties are assessed, in...
• Optimal stratification in outcome prediction using baseline information AI翻译
  作者：Yong, Florence H.; Tian, Lu; Yu, Sheng; Cai, Tianxi; Wei, L. J.
  作者单位：Harvard University; Stanford University; Tsinghua University
  摘要：A common practice in predictive medicine is to use current study data to construct a stratification procedure, which groups subjects according to baseline information and forms stratum-specific prevention or intervention strategies. A desirable stratification scheme would not only have small intra-stratum variation but also have a clinically meaningful discriminatory capability. We show how to obtain optimal stratification rules with such desirable properties from fitting a set of regression m...
• Default Bayesian analysis with global-local shrinkage priors AI翻译
  作者：Bhadra, Anindya; Datta, Jyotishka; Polson, Nicholas G.; Willard, Brandon
  作者单位：Purdue University System; Purdue University; Duke University; University of Chicago
  摘要：We provide a framework for assessing the default nature of a prior distribution using the property of regular variation, which we study for global-local shrinkage priors. In particular, we show that the horseshoe priors, originally designed to handle sparsity, are regularly varying and thus are appropriate for default Bayesian analysis. To illustrate our methodology, we discuss four problems of noninformative priors that have been shown to be highly informative for nonlinear functions. In each...