Least Squares Parameter Estimation for Sparse Functional Varying Coefficient Model

DOI

10.2991/jsta.2017.16.3.5How to use a DOI?

Keywords

Functional varying coefficient model; Longitudinal data Analysis; Rate of convergence; Regularization; Reproducing kernel Hilbert space; Sparsity.

Abstract

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Behdad Mostafaiy
AU  - Mohammad Reza Faridrohani
PY  - 2017
DA  - 2017/09/01
TI  - Least Squares Parameter Estimation for Sparse Functional Varying Coefficient Model
JO  - Journal of Statistical Theory and Applications
SP  - 337
EP  - 344
VL  - 16
IS  - 3
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.2017.16.3.5
DO  - 10.2991/jsta.2017.16.3.5
ID  - Mostafaiy2017
ER  -