Multivariate Regression and ANOVA Models with Outliers: A Comparative Approach
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...| Link(s) zu Dokument(en): | IHS Publikation |
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| Format: | IHS Series NonPeerReviewed |
| Sprache: | Englisch |
| Veröffentlicht: |
Institut für Höhere Studien
2003
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| Zusammenfassung: | 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.; |
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