A general approach to heteroscedastic linear regression

Leslie, David S. and Kohn, Robert and Nott, David J. (2007) A general approach to heteroscedastic linear regression. Statistics and Computing, 17 (2). pp. 131-146. ISSN 0960-3174

Full text not available from this repository.

Abstract

Our article presents a general treatment of the linear regression model, in which the error distribution is modelled nonparametrically and the error variances may be heteroscedastic, thus eliminating the need to transform the dependent variable in many data sets. The mean and variance components of the model may be either parametric or nonparametric, with parsimony achieved through variable selection and model averaging. A Bayesian approach is used for inference with priors that are data-based so that estimation can be carried out automatically with minimal input by the user. A Dirichlet process mixture prior is used to model the error distribution nonparametrically; when there are no regressors in the model, the method reduces to Bayesian density estimation, and we show that in this case the estimator compares favourably with a well-regarded plug-in density estimator. We also consider a method for checking the fit of the full model. The methodology is applied to a number of simulated and real examples and is shown to work well.

Item Type:
Journal Article
Journal or Publication Title:
Statistics and Computing
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
70763
Deposited By:
Deposited On:
12 Sep 2014 10:31
Refereed?:
Yes
Published?:
Published
Last Modified:
10 Nov 2020 02:56