Improved hypothesis testing in a general multivariate elliptical model
| Autor: | Tatiane F. N. Melo, Alexandre G. Patriota, Silvia Ferrari |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Statistics and Probability
Multivariate statistics Covariance matrix Applied Mathematics 0211 other engineering and technologies Regression analysis Sample (statistics) Multivariate normal distribution ESTATÍSTICA 02 engineering and technology 01 natural sciences 010104 statistics & probability Distribution (mathematics) Modeling and Simulation 021105 building & construction Statistics Mean vector 0101 mathematics Statistics Probability and Uncertainty Mathematics Statistical hypothesis testing |
| Zdroj: | Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP |
| Popis: | This paper investigates improved testing inferences under a general multivariate elliptical regression model. The model is very flexible in terms of the specification of the mean vector and the dispersion matrix, and of the choice of the error distribution. The error terms are allowed to follow a multivariate distribution in the class of the elliptical distributions, which has the multivariate normal and Student-t distributions as special cases. We obtain Skovgaard's adjusted likelihood ratio (LR) statistics and Barndorff-Nielsen's adjusted signed LR statistics and we compare the methods through simulations. The simulations suggest that the proposed tests display superior finite sample behaviour as compared to the standard tests. Two applications are presented in order to illustrate the methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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