Nonparametric estimation of mean and covariance structures for longitudinal data | Zendy

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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

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

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