Premium
Nonparametric estimation of mean and covariance structures for longitudinal data
Author(s) -
Lin Huazhen,
Pan Jianxin
Publication year - 2013
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.1002/cjs.11189
Subject(s) - nonparametric statistics , covariance , asymptotic distribution , consistency (knowledge bases) , nonparametric regression , smoothing , statistics , mathematics , normality , econometrics , longitudinal data , covariance function , analysis of covariance , estimation , computer science , data mining , estimator , engineering , systems engineering , geometry
Abstract In this article we propose a novel nonparametric regression method to model the mean and covariance structures for longitudinal data. A modification of local linear smoothing estimation techniques is used to estimate the parameters and unknown functions in the model. Theoretical properties including uniform consistency and asymptotic normality are studied under certain mild conditions. Simulation studies are carried out to evaluate the efficacy of the proposed method, and real data analysis is provided for illustration. The Canadian Journal of Statistics 41: 557–574; 2013 © 2013 Statistical Society of Canada