Semi-Parametric Generalized Estimating Equations with Kernel Smoother

Longitudinal studies are widely used in various fields, such as public health, clinic trials and financial data analysis. A major challenge for longitudinal studies is repeated measurements from each subject, which cause time dependent correlation within subjects. Generalized Estimating Equations can deal with correlated outcomes for longitudinal data through marginal effect. My model will base on Generalized Estimating Equations with semi-parametric approach, providing a flexible structure for regression models: coefficients for parametric covariates will be estimated and nuisance covariates will be fitted in kernel smoothers for non-parametric part. Profile kernel estimator and the seemingly unrelated kernel estimator (SUR) will be used to deliver consistent and efficient semi-parametric estimators comparing to parametric models. We provide simulation results for estimating semi-parametric models with one or multiple non-parametric terms. In application part, we would like to focus on financial market: a credit card loan data will be used with the payment information for each customer across 6 months, investigating whether gender, income, age or other factors will influence payment status significantly. Furthermore, we propose model comparisons to evaluate whether our model should be fitted based on different levels of factors, such as male and female or based on different types of estimating methods, such as parametric estimation or semi-parametric estimation.