Analysis of binary longitudinal data with time-varying effects

Seonghyun Jeong, Minjae Park and Taeyoung Park

Computational Statistics & Data Analysis, 2017, vol. 112, issue C, 145-153

Abstract: This paper considers the analysis of longitudinal data where a binary response variable is observed repeatedly for each subject over time. In analyzing such data, regression coefficients are commonly assumed constant over time, which may not properly account for the time-varying effects of some subject characteristics on a sequence of binary outcomes. This paper proposes a Bayesian method for the analysis of binary longitudinal data with time-varying regression coefficients and random effects to account for nonlinear subject-specific effects over time as well as between-subject variation. The proposed method facilitates posterior computation via the method of partial collapse and accommodates spatially inhomogeneous smoothness of nonparametric functions without overfitting via a basis search technique. The proposed method is illustrated with a simulated study and the binary longitudinal data from the German socioeconomic panel study.

Keywords: Longitudinal data; Probit mixed model; Nonparametric regression; Partial collapse; Repeated measures (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:112:y:2017:i:c:p:145-153

DOI: 10.1016/j.csda.2017.03.007

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