Semiparametric and Nonparametric Models for Correlated Data

• Activity Number: 288
• Type: Invited
• Date/Time: Wednesday, August 11, 2004 : 8:30 AM to 10:20 AM
• Sponsor: WNAR
• Author(s): Annie Qu*+ and Runze Li
• Companies: Oregon State University and Pennsylvania State University
• Keywords: longitudinal data ; smoothing spline ; goodness-of-fit ; nonparametric regression ; GEE ; quadratic inference function

Abstract

Estimating equation approaches is useful for correlated data because the likelihood function is often unknown or intractable. However, estimating equation approaches lacks objective functions for selecting the correct root in multiple root problems, and likelihood-type functions to produce inference functions. A general description is given of the quadratic inference function approach (Qu, et al. 2000), a semiparametric framework defined by a set of mean zero estimating functions, but differing from the standard estimating function approach in that there are more equations than unknown parameters. The quadratic inference function method provides efficient and robust estimation of parameters in longitudinal data settings, and inference functions for testing. Further, an efficient estimator using a nonparametric regression spline is developed, and a goodness-of-fit test is introduced. The asymptotic chi-squared test is useful for testing whether coefficients in nonparametric regression are time-varying or time-invariant.