Density estimation for incomplete data model
National Human Genome Center
Engr 4201 (Computer science conference room)
4400 University Drive, Fairfax, VA 22030
Time: 11:00 A.M. - 12:00 P.M.
Date: Friday, Mar 8, 2013
For incomplete data model, the commonly used density estimator of the underlying distribution cannot be computed directly, as the corresponding kernel requires all original data to be available. To estimate density function with such incomplete data model using only the observed data, we propose to use a conditional version of the kernel given the observed data. We study such kernel density estimator for several commonly used incomplete data models. Some large-sample properties of the proposed estimators are investigated.