Efficient Estimation of the Nonparametric Mean and Covariance Functions for Longitudinal and Sparse Functional Data

• 成果类型: Article
• 署名作者: Zhou, Ling; Lin, Huazhen; Liang, Hua
• 署名单位: Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China; George Washington University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2017.1356317
• 发表日期: 2018
• 页码: 1550-1564
• 关键词: Principal component analysis; convergence-rates; latent process; models; regression

摘要

We consider the estimation of mean and covariance functions for longitudinal and sparse functional data by using the full quasi-likelihood coupling a modification of the local kernel smoothing method. The proposed estimators are shown to be consistent, asymptotically normal, and semiparametrically efficient in terms of their linear functionals. Their superiority to the competitors is further illustrated numerically through simulation studies. The method is applied to analyze AIDS study and atmospheric study. Supplementary materials for this article are available online.