The Conditional Predictive Ordinate for the Normal Distribution
The conditional predictive ordinate (CPO) is a Bayesian diagnostic which detects surprising observations. It has been used in a variety of situations such as univariate samples, the multi-variate normal distribution and regression models. Results are presented about the most surprising observation w...
| Publié dans: | Journal of the Royal Statistical Society. Series B (Methodological). - Royal Statistical Society, 1948. - 52(1990), 1, Seite 175-184 |
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| Auteur principal: | |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
1990
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| Accès à la collection: | Journal of the Royal Statistical Society. Series B (Methodological) |
| Sujets: | Convex Hull Mahalanobis Distance Outliers Ratio Ordinate Regression Diagnostic Mathematics Applied sciences Information science Behavioral sciences |
| Résumé: | The conditional predictive ordinate (CPO) is a Bayesian diagnostic which detects surprising observations. It has been used in a variety of situations such as univariate samples, the multi-variate normal distribution and regression models. Results are presented about the most surprising observation which has minimum CPO. For the multivariate normal distribution it is shown that the most surprising observation must lie at one of the vertices of the convex hull. It is also shown that the observation with maximum Mahalanobis distance from the sample mean must lie on the convex hull. Results are given for the expected number of vertices on the convex hull when the sample is contaminated. An alternative, closely related diagnostic, the ratio ordinate measure, is presented. A numerical comparison of the two measures is given. |
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| ISSN: | 00359246 |