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Bayesian Inference in Multivariate Regression with Missing Observations on the Response Variables

Irwin Guttman and Ulrich Menzefricke
Journal of Business & Economic Statistics
Vol. 1, No. 3 (Jul., 1983), pp. 239-248
DOI: 10.2307/1391345
Stable URL: http://www.jstor.org/stable/1391345
Page Count: 10
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Bayesian Inference in Multivariate Regression with Missing Observations on the Response Variables
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Abstract

We discuss the case of the multivariate linear model Y = XB + E with Y an (n × p) matrix, and so on, when there are missing observations in the Y matrix in a so-called nested pattern. We propose an analysis that arises by incorporating the predictive density of the missing observations in determining the posterior distribution of B, and its mean and variance matrix. This involves us with matric-T variables. The resulting analysis is illustrated with some Canadian economic data.

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