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作者：Canale, A.; Lijoi, A.; Nipoti, B.; Prunster, I.
作者单位：University of Padua; Bocconi University; Trinity College Dublin
摘要：For the most popular discrete nonparametric models, beyond the Dirichlet process, the prior guess at the shape of the data-generating distribution, also known as the base measure, is assumed to be diffuse. Such a specification greatly simplifies the derivation of analytical results, allowing for a straightforward implementation of Bayesian nonparametric inferential procedures. However, in several applied problems the available prior information leads naturally to the incorporation of an atom i...
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作者：Eck, D. J.; Cook, R. D.
作者单位：University of Minnesota System; University of Minnesota Twin Cities
摘要：Envelope methodology can provide substantial efficiency gains in multivariate statistical problems, but in some applications the estimation of the envelope dimension can induce selection volatility that may mitigate those gains. Current envelope methodology does not account for the added variance that can result from this selection. In this article, we circumvent dimension selection volatility through the development of a weighted envelope estimator. Theoretical justification is given for our ...
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作者：Liu, Yukun; Li, Pengfei; Qin, Jing
作者单位：East China Normal University; University of Waterloo; National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID)
摘要：Capture-recapture experiments are widely used to collect data needed for estimating the abundance of a closed population. To account for heterogeneity in the capture probabilities, Huggins (1989) and Alho (1990) proposed a semiparametric model in which the capture probabilities are modelled parametrically and the distribution of individual characteristics is left unspecified. A conditional likelihood method was then proposed to obtain point estimates andWald-type confidence intervals for the a...
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作者：Srivastava, Sanvesh; Engelhardt, Barbara E.; Dunson, David B.
作者单位：University of Iowa; Princeton University; Duke University
摘要：Bayesian sparse factor models have proven useful for characterizing dependence in multivariate data, but scaling computation to large numbers of samples and dimensions is problematic. We propose expandable factor analysis for scalable inference in factor models when the number of factors is unknown. The method relies on a continuous shrinkage prior for efficient maximum a posteriori estimation of a low-rank and sparse loadings matrix. The structure of the prior leads to an estimation algorithm...
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作者：Kosmidis, I.; Guolo, A.; Varin, C.
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作者：Sherlock, Chris; Thiery, Alexandre H.; Lee, Anthony
作者单位：Lancaster University; National University of Singapore; University of Warwick
摘要：We consider a pseudo-marginal Metropolis-Hastings kernel P-m that is constructed using an average of m exchangeable random variables, and an analogous kernel P-s that averages s < m of these same random variables. Using an embedding technique to facilitate comparisons, we provide a lower bound for the asymptotic variance of any ergodic average associated with P-m in terms of the asymptotic variance of the corresponding ergodic average associated with P-s. We show that the bound is tight and di...
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作者：Hristache, M.; Patilea, V.
作者单位：Ecole Nationale de la Statistique et de l'Analyse de l'Information (ENSAI)
摘要：We consider a general statistical model defined by moment restrictions when data are missing at random. Using inverse probability weighting, we show that such a model is equivalent to a model for the observed variables only, augmented by a moment condition defined by the missingness mechanism. Our framework covers parametric and semiparametric mean regressions and quantile regressions. We allow for missing responses, missing covariates and any combination of them. The equivalence result sheds ...
