A moving blocks empirical likelihood method for longitudinal data

In the analysis of longitudinal or panel data, neglecting the serial correlations among the repeated measurements within subjects may lead to inefficient inference. In particular, when the number of repeated measurements is large, it may be desirable to model the serial correlations more generally....

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Bibliographic Details
Published inBiometrics Vol. 71; no. 3; pp. 616 - 624
Main Authors Qiu, Jin, Wu, Lang
Format Journal Article
LanguageEnglish
Published United States Blackwell Publishing Ltd 01.09.2015
International Biometric Society
Subjects
Algorithms
Antiviral Agents - therapeutic use
autocorrelation
BIOMETRIC METHODOLOGY
biometry
Computer Simulation
Data Interpretation, Statistical
Econometrics
Economic models
Empirical likelihood
equations
Estimating techniques
General estimating equation
HIV Infections - diagnosis
HIV Infections - drug therapy
HIV Infections - epidemiology
Humans
Likelihood Functions
Longitudinal data
Longitudinal Studies
Nonparametric method
Outcome Assessment (Health Care) - methods
Prevalence
Reproducibility of Results
Risk Assessment - methods
Sensitivity and Specificity
Serial correlation
Treatment Outcome
Nonparametric method
Empirical likelihood
Serial correlation
General estimating equation
Longitudinal data
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Summary:In the analysis of longitudinal or panel data, neglecting the serial correlations among the repeated measurements within subjects may lead to inefficient inference. In particular, when the number of repeated measurements is large, it may be desirable to model the serial correlations more generally. An appealing approach is to accommodate the serial correlations nonparametrically. In this article, we propose a moving blocks empirical likelihood method for general estimating equations. Asymptotic results are derived under sequential limits. Simulation studies are conducted to investigate the finite sample performances of the proposed methods and compare them with the elementwise and subject-wise empirical likelihood methods of Wang et al. (2010, Biometrika 97, 79–93) and the block empirical likelihood method of You et al. (2006, Can. J. Statist. 34, 79–96). An application to an AIDS longitudinal study is presented.
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ISSN:0006-341X
1541-0420
1541-0420
DOI:10.1111/biom.12317