Journal ArticleDOI
Bayes prediction in the linear model with spherically symmetric errors
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In this article, the authors show that when prior information is objective or in the conjugate family, the Bayes prediction density is the same as that when the density of the observations is normal, for any Z.About:
This article is published in Economics Letters.The article was published on 1987-01-01. It has received 29 citations till now. The article focuses on the topics: Linear regression & Random variable.read more
Citations
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Robust bayesian inference in elliptical regression models
TL;DR: In this article, the authors consider a possibly nonlinear regression model under any multivariate elliptical data density, and examine Bayesian posterior and predictive results, which are shown to be robust with respect to the specific choice of a sampling density within this elliptical class.
Journal ArticleDOI
Posterior inference on the degrees of freedom parameter in multivariate-t regression models
TL;DR: In this paper, the authors consider the nonlinear regression model with errors that follow the multivariate Student-t distribution with ν degrees of freedom and provide general conditions on the overall prior structure under which the prior of ν is not updated by the sample information.
Journal ArticleDOI
Bayesian Statistical Inference on Elliptical Matrix Distributions
Kai-Tai Fang,Runze Li +1 more
TL;DR: In this paper, the posterior distribution, posterior mean, and generalized maximim likelihood estimators of a class of elliptical distributions with parameters?and? were derived under a noninformative prior distribution.
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Bayes prediction in regressions with elliptical errors
TL;DR: In this article, the prediction problem for linear regression models with elliptical errors when the Bayes prior is non-informative is considered, and it is shown that the prediction density under the elliptical error assumption is exactly the same as that obtained with normally distributed errors.
Journal ArticleDOI
The emperor's new clothes: a critique of the multivariate t regression model
TL;DR: The authors show that the empirical implications of the multivariate Student t model are precisely the same as those of the Gaussian model, and hence the suggestion of a broader distributional representation of the data is spurious, and the claims of robustness are misleading.
References
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Book
Aspects of multivariate statistical theory
TL;DR: In this paper, the authors present a set of standard tests on Covariance Matrices and Mean Vectors, and test independence between k Sets of Variables and Canonical Correlation Analysis.
Journal ArticleDOI
An introduction to Bayesian inference in econometrics
TL;DR: The Univariate Normal Linear Regression Model (ULRRLR) as discussed by the authors is a well-known model for regression analysis in economics and has been used extensively in the literature.
Journal ArticleDOI
An Introduction to Bayesian Inference in Econometrics.
J. D. Sargan,Arnold Zellner +1 more
MonographDOI
Statistical Prediction Analysis
John Aitchison,I. R. Dunsmore +1 more
TL;DR: This paper presents a meta-modelling procedure that automates the very labor-intensive and therefore time-heavy and expensive and expensive process of manually cataloging and forecasting the distribution of distributions in a discrete-time manner.