Abstract #301884

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JSM 2003 Abstract #301884
Activity Number: 336
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #301884
Title: Formulations of Missing-Data Models and Likelihood-Based Inference
Author(s): Blake W. Whitten*+
Companies: University of California, Santa Barbara
Address: Dept. of Statistics, Santa Barbara, CA, 93106-3110,
Keywords: asymptotics ; maximum likelihood ; missing data
Abstract:

Maximum-likelihood estimation in missing-data problems is well-defined by a parametric model which explicitly accounts for the stochastic nature of the observed-data pattern imposed upon a complete-data random vector. We present such a model, a rarity in the missing-data literature. We exploit the model to develop conditions on both the complete data and on the missing-data mechanism sufficient to invoke classical MLE asymptotics in the IID case. These conditions constitute a new contribution to the theory of likelihood-based inference in missing-data problems. We present an example of erroneous reasoning from the literature to illustrate the danger of avoiding a formal model. The correct analysis is shown and connected to the aforementioned sufficient conditions. Inferential implications of the incorrect analysis are described both analytically and through simulation.

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