Abstract
This chapter presents the Partitioned GMM marginal model estimates for time-dependent covariates. It utilizes a recent test for valid moment conditions. Instead of grouping the valid moment conditions to obtain an average effect of the covariate on the response, a partitioning of the moment conditions is explored. This partitioning produces extra regression parameters for each covariate, with insight into each of the time-varying relationships inherent to longitudinal data. The moment conditions are grouped based on the time lag between the covariate and the response. Assume that the observations are correlated and the moment conditions are identified.
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Wilson, J.R., Vazquez-Arreola, E., Chen, (.DG. (2020). Partitioned GMM Logistic Regression Models for Longitudinal Data. In: Marginal Models in Analysis of Correlated Binary Data with Time Dependent Covariates. Emerging Topics in Statistics and Biostatistics . Springer, Cham. https://doi.org/10.1007/978-3-030-48904-5_5
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DOI: https://doi.org/10.1007/978-3-030-48904-5_5
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