文章基本信息

标题：Least Squares Parameter Estimation for Sparse Functional Varying Coefficient Model
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作者：Behdad Mostafaiy ; Mohammad Reza Faridrohani
期刊名称：Journal of Statistical Theory and Applications (JSTA)
电子版ISSN：1538-7887
出版年度：2017
卷号：16
期号：3
页码：339-346
DOI：10.2991/jsta.2017.16.3.5
语种：English
出版社：Atlantis Press
摘要：In the present paper, we study functional varying coefficient model in which both the response and the predictor are functions. We give estimates of the intercept and the slope functions in the case that the observations are sparse and noise-contaminated longitudinal data by using least squares representation of the model parameters. To estimate the parameter functions involved in the representation, we use a regularization method in some reproducing kernel Hilbert spaces. As we will see, our procedure is easy to implement. Also, we obtain the convergence rates of the estimators in the L2-sense. These convergence rates establish that the procedure performs well, especially, when sampling frequency or sample size increases.
关键词：Functional varying coefficient model; Longitudinal data Analysis; Rate of convergence; Regularization; Reproducing kernel Hilbert space; Sparsity.