🚀 go-pugleaf

Topic:     Bayesian linear regression with error terms that have 
		symmetric unimodal densities
Speaker:   Lawrence J. Brunner
Date:      Thursday, Jan. 18, 1990     4-5:00 p.m.
Place:     Rm. 1085, Sidney Smith Hall, 100 St. George Street, U of T

Abstract:
     Bayes methods are provided for a multiple linear regression model in which
the error terms have densities that are symmetric and unimodal at zero, but
whose form is otherwise unknown.  The posterior distribution of the vector or
regression coefficients is obtained.  Simulations are presented in which the
frequentist variability of the parameter estimates (posterior expectations)
is investigated. When the error terms have a heavy-tailed density, the
present method yields estimates of the true regression coefficients that are
much more accurate than the least-squares estimates.
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