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

被引：17

作者：

Zhou, Ling ^{[1
,2
]}

Lin, Huazhen ^{[1
,2
]}

Liang, Hua ^{[3
]}

机构：

[1] Southwestern Univ Finance & Econ, Ctr Stat Res, Chengdu, Sichuan, Peoples R China

[2] Southwestern Univ Finance & Econ, Sch Stat, Chengdu, Sichuan, Peoples R China

[3] George Washington Univ, Dept Stat, Washington, DC 20052 USA

来源：

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION | 2018年 / 113卷 / 524期

基金：

中国国家自然科学基金;

关键词：

Curse of dimensionality; Full quasi-likelihood function; Gaussian process; Longitudinal data; Nonparametric structure; Semiparametric efficiency; PRINCIPAL COMPONENT ANALYSIS; CONVERGENCE-RATES; LATENT PROCESS; MODELS; REGRESSION;

D O I：

10.1080/01621459.2017.1356317

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

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.

引用

页码：1550 / 1564

页数：15

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