Solving Generalised Estimating Equations With Missing Data Using Pseudo Maximum Likelihood Estimation Is Equivalent to Complete Case Analysis

Ziegler, Andreas und Kastner, C. (1998): Solving Generalised Estimating Equations With Missing Data Using Pseudo Maximum Likelihood Estimation Is Equivalent to Complete Case Analysis. Sonderforschungsbereich 386, Discussion Paper 128

Abstract

Arminger and Sobel(1990) proposed an approach to estimate mean- and covariance structures in the presence of missing data. These authors claimed that their method based on Pseudo Maximum Likelihood (PML) estimation may be applied if the data are missing at random (MAR) in the sense of Little and Rubin (1987). Rotnitzky and Robins (1995), however, stated that the PML approach may yield inconsistent estimates if the data are (MAR). We show that the adoption of the PML approach for mean- and covariance structures to mean structures in the presence of missing data as proposed by Ziegler (1994) is identical to the complete case (CC) estimator. Nevertheless, the PML approach has the computational advantage in that the association structure remains the same.

• Dokumententyp: Paper
• Fakultät: Mathematik, Informatik und Statistik > Statistik > Sonderforschungsbereich 386 ; Sonderforschungsbereiche > Sonderforschungsbereich 386
• Themengebiete: 500 Naturwissenschaften und Mathematik > 510 Mathematik
• Sprache: Englisch