Titlebook: Logistic Regression with Missing Values in the Covariates; Werner Vach Book 1994 Springer-Verlag New York, Inc. 1994 Conditional probabili

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Generalizations for More Than two Covariates mention that the robustness against violations of the MAR assumption remains valid of course, too. Thus we restrict our attention in this chapter to ML Estimation, Semiparametric ML Estimation, and Estimation of the Score Function.

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The Complete Data CaseLet be . a binary outcome variable, . a covariate with categories 1,…, . and . a covariate with categories 1,…, .. In a logistic model we assume . with parameter restrictions β. . 0 and β. . 0. Λ(.):= 1/(1 + .) denotes the logistic function. We consider the covariates as random variables, and parametrize their joint distribution by ..

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Quantitative Comparisons: Results of Finite Sample Size Simulation StudiesThe investigations of the last chapter were based on asymptotic arguments. It remains to show that the results of the comparisons are transferable to the finite sample size. Moreover, the properties of the methods themselves have been examined so far only asymptotically, and the estimation of variance is also based on asymptotic results.

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https://doi.org/10.1007/978-1-4612-2650-5Conditional probability; Finite; Likelihood; Logistic Regression; Variance; expectation–maximization algo