Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/71242
Authors: 
Polasek, Wolfgang
Year of Publication: 
2003
Series/Report no.: 
Reihe Ökonomie / Economics Series, Institut für Höhere Studien (IHS) 136
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 data of Fuller (1987): The results are similar but differ in their strength of their empirical evidence.
Subjects: 
multivariate regression
multivariate one-way ANOVA
outliers
Gibbs sampling
marginal likelihoods
sensitivity analysis
JEL: 
C11
C39
Document Type: 
Working Paper

Files in This Item:
File
Size
418.28 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.