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Testing of homogeneity of variance and autocorrelation coefficients of nonlinear mixed models with AR(1) errors based on M-estimation

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  • Huihui Sun

Abstract

Homogeneity of between-individual variance and autocorrelation coefficients is one of assumptions in the study of longitudinal data. However, the assumption could be challenging due to the complexity of the dataset. In the paper we propose and analyze nonlinear mixed models with AR(1) errors for longitudinal data, intend to introduce Huber's function in the log-likelihood function and get robust estimation, which may help to reduce the influence of outliers, by Fisher scoring method. Testing of homogeneity of variance among individuals and autocorrelation coefficients on the basis of Huber's M-estimation is studied later in the paper. Simulation studies are carried to assess performance of score test we proposed. Results obtained from plasma concentrations data are reported as an illustrative example.

Suggested Citation

  • Huihui Sun, 2017. "Testing of homogeneity of variance and autocorrelation coefficients of nonlinear mixed models with AR(1) errors based on M-estimation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(2), pages 362-375, January.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:2:p:362-375
    DOI: 10.1080/02664763.2016.1169259
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    References listed on IDEAS

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    1. Russo, Cibele M. & Paula, Gilberto A. & Aoki, Reiko, 2009. "Influence diagnostics in nonlinear mixed-effects elliptical models," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4143-4156, October.
    2. Beow Yong Yeap & Marie Davidian, 2001. "Robust Two‐Stage Estimation in Hierarchical Nonlinear Models," Biometrics, The International Biometric Society, vol. 57(1), pages 266-272, March.
    3. Domowitz, Ian & White, Halbert, 1982. "Misspecified models with dependent observations," Journal of Econometrics, Elsevier, vol. 20(1), pages 35-58, October.
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