Wavelet-Based Functional Linear Mixed Models for Panel Study Data

• Activity Number: 428
• Type: Contributed
• Date/Time: Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Elizabeth J. Malloy*+ and Jeffrey S. Morris and Sara D. Adar and Helen Suh and Dianne R. Gold and Brent A. Coull
• Companies: American University and MD Anderson Cancer Center and University of Michigan School of Public Health and Harvard School of Public Health and Harvard School of Public Health and Harvard School of Public Health
• Keywords: Functional Data Analysis ; Particulate Matter ; Distributed Lag Model ; Panel Study ; Air Pollution ; Wavelets

Abstract

In many applications researchers are interested in using discretely measured time-varying exposure data as covariates in a regression setting. An example is in panel studies of the health effects of particulate matter, where particle levels are measured over time. In such studies there are many more values of the exposure data than observations in the data set so that regularization of the corresponding functional regression coefficient is necessary. Additional issues are the possibility of exposure measurement error and the need to incorporate additional potential confounders, such as meteorological or co-pollutant measures, which may have effects that vary over time. To accommodate these features, we develop wavelet-based linear mixed distributed lag models that incorporate repeated measures of functional data as covariates into a linear mixed model.