title: Kernel Smoothing Density Estimation when Group Membership is Subject to Missing.
abstract: Density function is a fundamental concept in data analysis. Nonparametric methods including kernel smoothing estimate are available if the data is completely observed. However, in studies such as diagnostic studies following a two-stage design the membership of some of the subjects may be missing. Simply ignoring those subjects with unknown membership is valid only in the MCAR situation. In this paper, we consider kernel smoothing estimate of the density functions, using the inverse probability approaches to address the missing values. We illustrate the approaches with simulation studies and real study data in mental health.
authors: Department of Biostatistics and Computational Biology, University of Rochester Medical Center, 601 Elmwood Ave, Box 630, Rochester, NY 14642, U.S.A.
authors: Wan
authors: W
authors: Tang
authors: Hua
authors: H
authors: He
authors: Douglas
authors: D
authors: Gunzler