NDLI: Empirical likelihood analysis of longitudinal data involving within-subject correlation

Content Provider	Springer Nature Link
Author	Hu, Shuang Lin, Lu
Copyright Year	2012
Abstract	In this paper we use profile empirical likelihood to construct confidence regions for regression coefficients in partially linear model with longitudinal data. The main contribution is that the within-subject correlation is considered to improve estimation efficiency. We suppose a semi-parametric structure for the covariances of observation errors in each subject and employ both the first order and the second order moment conditions of the observation errors to construct the estimating equations. Although there are nonparametric estimators, the empirical log-likelihood ratio statistic still tends to a standard χ p 2 variable in distribution after the nuisance parameters are profiled away. A data simulation is also conducted.
Ending Page	744
Page Count	14
Starting Page	731
File Format	PDF
ISSN	01689673
e-ISSN	16183932
Journal	Acta Mathematicae Applicatae Sinica
Issue Number	4
Volume Number	28
Language	English
Publisher	Institute of Applied Mathematics, Chinese Academy of Sciences and Chinese Mathematical Society
Publisher Date	2012-11-21
Publisher Place	Heildeberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	partially linear model Asymptotic properties of estimators within-subject correlation Asymptotic properties Point estimation Estimation Theoretical, Mathematical and Computational Physics Applications of Mathematics Math Applications in Computer Science empirical-likelihood longitudinal data
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics

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