Regression models involving nonlinear effects with missing data. A sequential modeling approach using Bayesian estimation.

Autor/inn/en	Lüdtke, Oliver; Robitzsch, Alexander; West, Stephen G.
Titel	Regression models involving nonlinear effects with missing data. A sequential modeling approach using Bayesian estimation.
Quelle	In: Psychological methods, 25 (2020) 2, S. 157-181 PDF als Volltext Link als defekt meldenVerfügbarkeit
Sprache	englisch
Dokumenttyp	online; gedruckt; Zeitschriftenaufsatz
ISSN	1082-989X; 1939-1463
DOI	10.1037/met0000233
Schlagwörter	Methode; Anwendungsprogramm; Fehlertoleranz; Datenauswertung; Datengewinnung; Softwaretechnologie; Bayes-Statistik; Statistik; Daten; Modellierung; Benutzerfreundlichkeit; Datenverarbeitung
Abstract	When estimating multiple regression models with incomplete predictor variables, it is necessary to specify a joint distribution for the predictor variables. A convenient assumption is that this distribution is a joint normal distribution, the default in many statistical software packages. This distribution will in general be misspecified if the predictors with missing data have nonlinear effects (e.g., x2) or are included in interaction terms (e.g., x·z). In the present article, [the authors] discuss a sequential modeling approach that can be applied to decompose the joint distribution of the variables into 2 parts: (a) a part that is due to the model of interest and (b) a part that is due to the model for the incomplete predictors. [The authors] demonstrate how the sequential modeling approach can be used to implement a multiple imputation strategy based on Bayesian estimation techniques that can accommodate rather complex substantive regression models with nonlinear effects and also allows a flexible treatment of auxiliary variables. In 4 simulation studies, [the authors] showed that the sequential modeling approach can be applied to estimate nonlinear effects in regression models with missing values on continuous, categorical, or skewed predictor variables under a broad range of conditions and investigated the robustness of the proposed approach against distributional misspecifications. [The authors] developed the R package mdmb, which facilitates a user-friendly application of the sequential modeling approach, and [...] present a real-data example that illustrates the flexibility of the software. (Orig.).
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2021/2