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. We propose estimation methods for a general semiparametric density model and develop computational procedures under different situations. We also present simulation results and real data examples.