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A novel robust approach for analysis of longitudinal data

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  • Zhang, Yuexia
  • Qin, Guoyou
  • Zhu, Zhongyi
  • Xu, Wanghong

Abstract

A new robust estimating equation approach for analysis of longitudinal data is developed. To achieve robustness against outliers, a novel approach which corrects the bias induced by outliers through centralizing the covariate matrix in the estimating equation is proposed. The covariates are centralized by subtracting their conditional expectations and the conditional expectations can be estimated by using the local linear smoothing method. The consistency and asymptotic normality of the proposed estimator are established under some regularity conditions. Extensive simulation studies show that the proposed method is robust, has a high efficiency, and is not limited to some specific error distributions. In the end, the proposed method is applied to the longitudinal study of prevalent patients with type 2 diabetes and confirms the effectiveness of dietary fibre intake in reducing glycolated hemoglobin A1c level.

Suggested Citation

  • Zhang, Yuexia & Qin, Guoyou & Zhu, Zhongyi & Xu, Wanghong, 2019. "A novel robust approach for analysis of longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 83-95.
    • Handle: RePEc:eee:csdana:v:138:y:2019:i:c:p:83-95
    DOI: 10.1016/j.csda.2019.04.002
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    References listed on IDEAS

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    Cited by:

    1. 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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