Nearly nonparametric multivariate density estimates that incorporate marginal parametric density information Academic Article

abstract

• When data analysts have multivariate data, often they have partial knowledge about the form of the marginal densities, but frequently they have little information about the bivariate and higher dimensional densities. This article provides nonparametric estimators that nearly equal the MLE estimates for the marginal densities while being close to the kernel nonparametric density estimates for the joint density estimates, provided that the assumption about the marginal densities is correct. The motivation for this article came from recollections of a 15-year-old conversation with Ingram Olkin where the problem at hand was how to model multivariate data with fixed marginals but with a flexible and rich multivariate structure. 2003 Taylor & Francis Group, LLC.

authors

• Park, Eun

published proceedings

• AMERICAN STATISTICIAN

author list (cited authors)

• Spiegelman, C., & Park, E. S.

citation count

• 3

complete list of authors

• Spiegelman, C||Park, ES

publication date

• August 2003

publisher

• Taylor & Francis  Publisher

published in

• American Statistician  Journal

keywords

• Kernel
• Parametric Start
• Penalized Likelihood
• Semiparametric

Digital Object Identifier (DOI)

• 10.1198/0003130031955

start page

• 183

end page

• 188

volume

• 57

issue

• 3

URL

• http://dx.doi.org/10.1198/0003130031955