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st: Evaluating likelihood ratio tests at specified parameter values


From   David Kling <klingd@reed.edu>
To   statalist@hsphsun2.harvard.edu
Subject   st: Evaluating likelihood ratio tests at specified parameter values
Date   Mon, 21 Aug 2006 00:24:02 -0700

I'm trying to implement a method for conducting likelihood ratio tests on models fit using data produced via multiple imputation. The method is described in detail in Meng and Rubin (1992) and reviewed in Schafer (1997). The only difficult part (from my point of view) of implementing the procedure is evaluating a likelihood ratio test at the mean values of the model parameters (the mean of the m parameter vectors, one from each of the m data sets generated during multiple imputation) for each of the m data sets. I'm interested in learning whether it is possible to evaluate the log-likelihood of a model (specifically, a glm with a gaussian link) at user-specified values of the parameters without resorting to writing my own program to do the job.

Thanks!

David Kling

References:

Meng, X.L. and Donald Rubin, "Performing Likelihood Ratio Tests with Multiply-Imputed Data Sets" Biometrika 79 pp 103-111 (1992).

Schafer, J.L., Analysis of Incomplete Multivariate Data. New York, NY: Chapman & Hall (1997)

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