Skip Navigation
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Your Environment. Your Health.

Publication Detail

Title: Misinformation in the conjugate prior for the linear model with implications for free-knot spline modelling.

Authors: Paciorek, Christopher J

Published In Bayesian Anal, (2006)

Abstract: In the conjugate prior for the normal linear model, the prior variance for the coefficients is a multiple of the error variance parameter. However, if the prior mean for the coefficients is poorly chosen, the posterior distribution of the model can be seriously distorted because of prior dependence between the coefficients and error variance. In particular, the error variance will be overestimated, as will the posterior variance of the coefficients. This occurs because the prior mean, which can be thought of as a weighted pseudo-observation, is an outlier with respect to the real observations. While this situation will be easily noticed and avoided in simple models, in more complicated models, the effect can be easily overlooked. The issue arises in the unit information (UI) prior, a conjugate prior in which the prior contributes information equal to that in one observation. In particular, a successful Bayesian nonparametric regression model - Bayesian Adaptive Regression Splines (BARS) - that relies on the UI prior for its model selection step suffers from this problem, and addressing the problem within the Bayesian paradigm alters the penalty on model dimensionality.

PubMed ID: 18185854 Exiting the NIEHS site

MeSH Terms: No MeSH terms associated with this publication

Back
to Top