Rendering parametric procedures more robust by empirically tilting the model
| Autor: | Peter Hall, Brett Presnell, Edwin Choi |
|---|---|
| Rok vydání: | 2000 |
| Předmět: |
Statistics and Probability
Kullback–Leibler divergence Applied Mathematics General Mathematics Score Agricultural and Biological Sciences (miscellaneous) Distance measures Rendering (computer graphics) Exponential family Robustness (computer science) Statistics Statistics Probability and Uncertainty Hellinger distance General Agricultural and Biological Sciences Algorithm Mathematics Parametric statistics |
| Zdroj: | Biometrika. 87:453-465 |
| ISSN: | 1464-3510 0006-3444 |
| DOI: | 10.1093/biomet/87.2.453 |
| Popis: | SUMMARY We suggest methods for tilting a likelihood so as to enhance the robustness of maximum likelihood procedures. From the viewpoint of computation, tilting amounts to choosing unequal weights for the score function in such a way as to maximise likelihood subject to moving a given distance from equally weighted scores. Empirical methods, based on standard parametric Q-Q plots, are used to determine the appropriate amount of tilting. Distance may be measured in a variety of ways, and we devote particular attention to power-divergence approaches. In this context, one of the two Kullback-Leibler distance measures is shown to be advantageous. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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