A0198
Title: Semi-parametric density models
Authors: Yuedong Wang - University of California - Santa Barbara (United States) [presenting]
Abstract: Maximum likelihood estimation within a parametric family and nonparametric estimation are two traditional approaches for density estimation. Sometimes, it is advantageous to model some components of the density function parametrically while leaving other components unspecified. Estimation methods are proposed for a general semiparametric density model, and computational procedures are developed under different situations. Simulation results and real data examples are also presented.
