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Parsimonious Mean-Covariance Modeling for Longitudinal Data with ARMA Errors

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Abstract

Based on the generalized estimating equation approach, the authors propose a parsimonious mean-covariance model for longitudinal data with autoregressive and moving average error process, which not only unites the existing autoregressive Cholesky factor model and moving average Cholesky factor model but also provides a wide variety of structures of covariance matrix. The resulting estimators for the regression coefficients in both the mean and the covariance are shown to be consistent and asymptotically normally distributed under mild conditions. The authors demonstrate the effectiveness, parsimoniousness and desirable performance of the proposed approach by analyzing the CD4+ cell counts data set and conducting extensive simulations.

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Authors and Affiliations

  1. Department of Statistics and Finance, University of Science and Technology of China, Hefei, 230026, China

    Jiangli Wang, Yu Chen & Weiping Zhang

Authors
  1. Jiangli Wang
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  2. Yu Chen
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  3. Weiping Zhang
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Corresponding author

Correspondence to Weiping Zhang.

Additional information

This work has been supported by the National Key Research and Development Plan under Grant No. 2016YFC0800100 and the National Science Foundation of China under Grant Nos. 11671374, 71771203, 71631006.

This paper was recommended for publication by Editor YU Zhangsheng.

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Wang, J., Chen, Y. & Zhang, W. Parsimonious Mean-Covariance Modeling for Longitudinal Data with ARMA Errors. J Syst Sci Complex 32, 1675–1692 (2019). https://doi.org/10.1007/s11424-019-7354-6

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  • DOI: https://doi.org/10.1007/s11424-019-7354-6

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