Abstract #302167

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JSM 2003 Abstract #302167
Activity Number: 336
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #302167
Title: A General Covariance Formula for Hypothesis Testing Using Randomly Incomplete Data
Author(s): Tejas A. Desai*+ and Pranab K. Sen
Companies: University of North Carolina and University of North Carolina, Chapel Hill
Address: 140 BPW Club Rd., Carrboro, NC, 27510-2548,
Keywords: multiple imputation ; missing data ; maximum likelihood estimation ; EM algorithm
Abstract:

An incomplete-data mle is not used as often as possible for hypothesis testing using randomly incomplete data. In particular, it is passed over for a multiple imputation estimate. We present a general framework for missing data, which allows us to derive a general asymptotic covariance formula for the mle, which allows hypothesis testing without resorting to imputation. We present some examples wherein we compare the mle with multiple imputation.

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