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A moving blocks empirical likelihood method for longitudinal data

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  • Jin Qiu
  • Lang Wu

Abstract

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.

Suggested Citation

  • Jin Qiu & Lang Wu, 2015. "A moving blocks empirical likelihood method for longitudinal data," Biometrics, The International Biometric Society, vol. 71(3), pages 616-624, September.
    • Handle: RePEc:bla:biomet:v:71:y:2015:i:3:p:616-624
    DOI: 10.1111/biom.12317
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    References listed on IDEAS

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    8. Hansen, Lars Peter & Singleton, Kenneth J, 1982. "Generalized Instrumental Variables Estimation of Nonlinear Rational Expectations Models," Econometrica, Econometric Society, vol. 50(5), pages 1269-1286, September.
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    Cited by:

    1. Wang, Kangning & Li, Shaomin & Sun, Xiaofei & Lin, Lu, 2019. "Modal regression statistical inference for longitudinal data semivarying coefficient models: Generalized estimating equations, empirical likelihood and variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 257-276.
    2. Qiu, Jin & Ma, Qing & Wu, Lang, 2019. "A moving blocks empirical likelihood method for panel linear fixed effects models with serial correlations and cross-sectional dependences," Economic Modelling, Elsevier, vol. 83(C), pages 394-405.
    3. Zhang, Yuexia & Qin, Guoyou & Zhu, Zhongyi & Zhang, Jiajia, 2022. "Empirical likelihood inference for longitudinal data with covariate measurement errors: An application to the LEAN study," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).

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