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- W2012956124 abstract "Abstract In a general parametric setup, a multivariate regression model is considered when responses may be missing at random while the explanatory variables and covariates are completely observed. Asymptotic optimality properties of maximum likelihood estimators for such models are linked to the Fisher information matrix for the parameters. It is shown that the information matrix is well defined for the missing-at-random model and that it plays the same role as in the complete-data linear models. Applications of the methodologic developments in hypothesis-testing problems, without any imputation of missing data, are illustrated. Some simulation results comparing the proposed method with Rubin's multiple imputation method are presented." @default.
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- W2012956124 date "2008-11-01" @default.
- W2012956124 modified "2023-09-25" @default.
- W2012956124 title "Information attainable in some randomly incomplete multivariate response models" @default.
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- W2012956124 doi "https://doi.org/10.1016/j.jspi.2007.02.015" @default.
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