Quadratic inference functions for partially linear single-index models with longitudinal data

Abstract

In this paper, we consider the partially linear single-index models with longitudinal data. We propose the bias-corrected quadratic inference function (QIF) method to estimate the parameters in the model by accounting for the within-subject correlation. Asymptotic properties for the proposed estimation methods are demonstrated. A generalized likelihood ratio test is established to test the linearity of the nonparametric part. Under the null hypotheses, the test statistic follows asymptotically a χ2 distribution. We also evaluate the finite sample performance of the proposed methods via Monte Carlo simulation studies and a real data analysis.