Abstract:
We consider maximum likelihood (ML) estimation of mean and covariance structure models when data are missing. Expectation maximization (EM), generalized expectation maximization (GEM), Fletcher-Powell, and Fisherscoring algorithms are described for parameter estimation. It is shown how the machinery within a software that handles the complete data problem can be utilized to implement each algorithm. A numerical di#erentiation method for obtaining the observed information matrix and the standard errors is given. This method too uses the complete data program machinery. The likelihood ratio test is discussed for testing hypotheses. Three examples are used to compare the cost of the four algorithms mentioned above, as well as to illustrate the standard error estimation and the test of hypothesis considered. The sensitivity of the ML estimates as well as the mean imputed and listwise deletion estimates to missing data mechanisms is investigated using three artificial data sets that are mis...
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