Abstract
This article utilizes bootstrap quasi-likelihood (QL) to model sparse functional data. The proposed method combines parallel block bootstrap and QL to fit the functional data. The parameter space is considered as a finite-dimensional space through a certain optimization rule. Statistical errors of the proposed method are discussed. Some asymptotic properties of the method are established under several mild conditions as well. Several simulations are conducted to examine the finite-sample performance of the method. The performance is also demonstrated by analysing real data.
Acknowledgments
The author would like to thanks the Editor and anonymous referees for their insightful comments and constructive suggestions which greatly improve this article.
Disclosure statement
No potential conflict of interest was reported by the author(s).