Nonparametric estimation of dynamic conditional correlation functions with longitudinal data

Oregon Convention Center 

An important objective in longitudinal analysis is to quantify the dynamic dependence structure between different outcome variables conditioning on a set of time-varying covariates. Existing nonparametric estimation methods do not take the dynamic dependence structure on the covariates into consideration. We propose a series of different approaches to estimate the time-varying conditional correlation functions based on kernel smoothing and structured nonparametric models for the conditional mean, variance and covariance functions, and construct their pointwise confidence intervals using a resampling-subject bootstrap procedure. We investigate the statistical properties of these smoothing estimators through a simulation study and apply these estimation and inference procedures to the Coronary Artery Risk Development in Young Adults (CARDIA) Study. Our findings suggest that the correlation of cardiovascular risk factors for young adults may change with age and other covariates.

Functional correlation function

Conditional correlation



Nonparametric estimation

Varying coefficient model 

Biometrics Section