Title: Inferences in Binary Dynamic Fixed Models in a Semi-parametric Setup

Author(s): Brajendra C. Sutradhar and Nan Zheng
Issue: Volume 80 Series B Part 2 Year 2018
Pages: 263 -- 291
In a longitudinal setup, the so-called generalized estimating equations approach was a popular inference technique to obtain efficient regression estimates until it was discovered that this approach may in fact yield less efficient estimates than an independence assumption-based estimating equation approach. In this paper, we revisit this inference issue in a semi-parametric longitudinal setup for binary data and find that the semi-parametric generalized estimating equations also encounter similar efficiency drawbacks when compared with independence assumption-based approach. This makes the generalized estimating equations approach unacceptable for correlated data analysis. We analyze the repeated binary data by fitting a semi-parametric binary dynamic model. The non-parametric function and the regression parameters involved in the semi-parametric regression function are estimated by using a semi-parametric generalized quasi-likelihood and a semi-parametric quasi-likelihood approach, respectively, whereas the dynamic dependence, that is, the correlation index parameter of the model is estimated by a semiparametric method of moments. Asymptotic and finite sample properties of the estimators are discussed. The proposed model and the estimation methodology are also illustrated by reanalyzing the well-known respiratory disease data.
AMS (2000) subject classification. Primary 62H12; Secondary 62F10, 62F12.
Keywords and phrases: Dynamic models for repeated binary responses, GEE approach in semi-parametric setup, Non-parametric function in secondary covariates, Parametric regression in primary covariates, Semi-parametric quasilikelihood and semi-parametric generalized quasi-likelihood estimation, Time dependent covariates 1
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