Presented by: 
Prof. Denis Leung, Ethel Raybould Visiting Fellow
Date: 
Wed 27 Jun, 2:00 pm - 3:00 pm
Venue: 
Room 442, Priestly Building Number 67

CARM/CSTAT Seminar Series

Prof. Denis Leung, Ethel Raybould Visiting Fellow

School of Economics, Singapore Management University

‘Shrinkage empirical likelihood estimator in longitudinal analysis with time-dependent covariates- An application to modelling the health of Filipino children’

The method of generalized estimating equations (GEE) is a popular tool in analysing longitudinal  (panel) data. Often, the covariates collected are time-dependent in nature, e.g., age, relapse status, monthly income. When using GEE to analyse longitudinal data with time-dependent covariates, crucial assumptions about the covariates are necessary for valid inferences to be drawn. When those assumptions do not hold or cannot be verified, Pepe and Andeson (1994) advocated using an independence working correlation assumption in the GEE model as a robust approach. We propose a method that extracts additional information from the estimating equations that have been excluded by the independence assumption. The method always includes the estimating equations under the independence assumption and the contribution from the remaining estimating equations is weighted according to the likelihood of each equation being a consistent estimating equation and the information it carries. Asymptotically, the method is  equivalent to using the largest set of consistent estimating equations for estimation. We apply the method to a longitudinal study of the health of a group of Filipino children.