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Biometrika 1976 63(1):51-58; doi:10.1093/biomet/63.1.51
© 1976 by Biometrika Trust
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Bayesian analysis of regression problems

M. GOLDSTEIN

Department of Mathematical Statistics, University of Hull

A general Bayesian formulation of the regression problem is considered, which derives from a direct specification of a prior distribution for the unknown joint probability distribution of the random variables. The resulting estimators are related to the least squares and ridge estimators of the regression coefficients.

Key Words: Bayesian estimation • Choice of regressor variables • Least squares estimator • Prediction • Ridge regression


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