An introduction to missing data in statistical applications is given in the beginning. The main part of the book deals with selection models for nonignorable missing data. The theory of selection models is described and illustrated by examples. Maximum Likelihood as well as Bayesian estimation approaches are discussed. A selection model with a nonparametric missing model that allows to treat flexible missing patterns is developed. This approach is unique in literature. The proposed model is extended to a model for longitudinal data.
Frankfurt am Main, Berlin, Bern, Bruxelles, New York, Oxford, Wien, 2005. 128 pp., num. graphs
Contents: Introduction to missing data problems in general – Selection models for nonignorable missing data – Discussion
of Maximum Likelihood and Bayesian estimation routines – Development of a selection model with a nonparametric missing model
– Extension of the latter to a model for longitudinal data.