Nearly Nonparametric Multivariate Density Estimates that Incorporate Marginal Parametric Density Information.
A method for modeling multivariate data with fixed marginals that preserves a rich and flexible multivariate structure is presented. Nonparametric estimators that are almost equal to the MLE estimates for the marginal densities and remain close to the kernel nonparametric density estimates for the...
| Publicado en: | American Statistician Vol. 57; no. 3; pp. 183 - 189 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
American Statistical Association
August 2003
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | A method for modeling multivariate data with fixed marginals that preserves a rich and flexible multivariate structure is presented. Nonparametric estimators that are almost equal to the MLE estimates for the marginal densities and remain close to the kernel nonparametric density estimates for the joint density estimates, given that the assumption concerning the marginal densities is correct, are demonstrated. |
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