Multivariate Regression and ANOVA Models with Outliers: A Comparative Approach

Polasek, Wolfgang (September 2003) Multivariate Regression and ANOVA Models with Outliers: A Comparative Approach. IHS Economics Series 136

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Abstract or Table of Contents

Abstract: Assuming a normal-Wishart modelling framework we compare two methods for finding outliers in a multivariate regression (MR) system. One method is the add-1-dummy approach which needs fewer parameters and a model choice criterion while the other method estimates the outlier probability for each observation by a Bernoulli mixing outlier location shift model. For the simple add-1-dummy model the Bayes factors and the posterior probabilities can be calculated explicitly. In the probabilistic mixing model we show how the posterior distribution can be obtained by a Gibbs sampling algorithm. The number of outliers is determined using the marginal likelihood criterion. The methods are compared for test scores of language examination dataof Fuller (1987): The results are similar but differ in their strength of their empirical evidence.;

Item Type: IHS Series
Keywords: 'Multivariate regression' 'Multivariate one-way ANOVA' 'Outliers' 'Gibbs sampling' 'Marginal likelihoods' 'Sensitivity analysis'
Classification Codes (e.g. JEL): C11, C39
Status: Published
Date Deposited: 26 Sep 2014 10:37
Last Modified: 23 Jul 2017 04:22
URI: http://irihs.ihs.ac.at/id/eprint/1510

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