RT Generic
T1 Robust bayesian inference in empirical regression models
A1 Osiewalski, Jacek
A1 Steel, Mark F.J.
A2 Universidad Carlos III de Madrid. Departamento de Economía, 
AB Broadening the stochastic assumptions on the error terms of regression models was prompted by the analysis of linear multivariate t models in Zellner (1976). We consider a possible non-linear regression model under any multivariate elliptical data density, and examine Bayesian posterior and productive results. The latter are shown to be robust with respect to the specific choice of a sampling density within this elliptical class. In particular, sufficient conditions for such model robustness are that we single out a precision factor T2 on which we can specify an improper prior density. Apart from the posterior distribution of this nuisance parameter T 2, the entire analysis will then be completely unaffected by departures from Normality. Similar results hold in finite mixtures of such elliptical densities, which can be used to average out specification uncertainty.
SN 2340-5031
YR 1991
FD 1991-11
LK https://hdl.handle.net/10016/2814
UL https://hdl.handle.net/10016/2814
LA eng
DS e-Archivo
RD 1 may. 2024