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Nonparametric estimation of dynamic conditional correlation functions with longitudinal data

Abstract Number:

2538 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Haiou Li (1), Hongbin Fang (2), Xin Tian (3), Colin Wu (4)

Institutions:

(1) National Heart, Lung & Blood Institute, N/A, (2) Georgetown University, N/A, (3) NIH/NHLBI-Office of Biostatistics Research, N/A, (4) National Heart, Lung & Blood Institute, Office of Biostatistics Research, N/A

Co-Author(s):

Hongbin Fang  
Georgetown University
Xin Tian  
NIH/NHLBI-Office of Biostatistics Research
Colin Wu  
National Heart, Lung & Blood Institute, Office of Biostatistics Research

First Author:

Haiou Li  
National Heart, Lung & Blood Institute

Presenting Author:

Haiou Li  
N/A

Abstract Text:

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.

Keywords:

Functional correlation function|Conditional correlation| |Nonparametric estimation|Varying coefficient model|

Sponsors:

Biometrics Section

Tracks:

Longitudinal/Correlated Data

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