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统计与计量经济学中的贝叶斯和似然方法：向乔治·A.巴纳德致敬的论文集AI 解读

Introduction. A Survey of George Barnard's Statistical Work. (D.V. Lindley). Foundations (Contributors: A.P. Dempster B.M. Hill J.S. Hodges D.A. Lane). Selected Applications (Contributors: S. Chib, S.R. Jammalamadaka, R.C. Tiwari L. Denby, R. Gnanadesikan, J.R. Kettenring, J.W. Suzansky S.E. Fienberg Y. Hiatovsky H. Tsurumi). Selected Inference Problems (Contributors: M.J. Bayarri, M.H. DeGroot L.D. Broemeling, M.S. Son, H.I. Hamdy C.-H. Chen, J.M. Dickey G.T. Duncan M.…

从预测视角识别与刻画回归中的影响观测AI 解读

Assuming a set of observations is available from the general linear model, and assuming prior information about parameters, we propose a method of assessing the influence of specified subsets of the data when the goal is to predict future observations.

面向预测的模型选择方法AI 解读

This article offers a synthesis of Bayesian and sample-reuse approaches to the problem of high structure model selection geared to prediction. Similar methods are used for low structure models. Nested and nonnested paradigms are discussed and examples given.

预测中的样本重复利用方法及其应用AI 解读

An account is given of a recently devised method of prediction based on sample reuse techniques. It is most useful in low structure data paradigms that involve minimal assumptions. A series of applications demonstrating the technique is presented.

随机效应模型的预测方法AI 解读

Two simple estimators are derived here for the means of the random effect model by means of predictive sample reuse. They are applied to two sets of data in the literature and compared with several other procedures. The mixed model is also discussed.

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均一协方差情形下的估计AI 解读

Summary There are three basic methods (confidence, fiducial, Bayesian) of obtaining interval estimates or estimating regions for the parameters of the multivariate normal distribution, none of which need necessarily yield a unique solution, although this has been claimed as a property of the fiducial argument. It has been previously demonstrated that they are not consistent with one another, i.e. they yield different regions for the same parameters. In this paper we show…

多元正态分类的后验优势比AI 解读

Summary A class of Bayesian methods is presented for finding the posterior odds or probability that a particular observation belongs to one of k multivariate normal populations. Solutions are given for various assumptions on the parameters of the k populations as well as for known and unknown prior probabilities. An application to genetics is included.

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特殊协方差结构下的多元方差分析AI 解读

Multivariate analysis of variance tests are developed for situations where the underlying covariance structure is uniform (equal variances and covariances) in terms of statistics analogous to Hotelling's T 2 and T 2 0. Extensions are made to several populations as well as to blocks of uniform covariance matrices. A special case, which is typical of the test procedures given here, is the problem of testing whether the mean vector of a bivariate normal distribution is equal to…

多元正态分布参数的后验分布AI 解读

SUMMARY A class of Bayes posterior distributions is obtained for the parameters of the multivariate normal distribution. These are compared with their fiducial and confidence counterparts.