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Perturbation diagnostics of autocorrelation coefficients in non linear mixed-effects models with AR(1) errors based on M-estimation

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  • Huihui Sun

Abstract

In this work we propose and analyze non linear mixed-effects models for longitudinal data, which are widely used in the fields of economics, biopharmaceuticals, agriculture, and so on. A robust method to obtain maximum likelihood estimates for the parameters is presented, as well as perturbation diagnostics of autocorrelation coefficient in non linear models based on robust estimates and influence curvature. The obtained results are illustrated by plasma concentrations data presented in Davidian and Giltinan, which was analyzed under the non robust situation.

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  • Huihui Sun, 2017. "Perturbation diagnostics of autocorrelation coefficients in non linear mixed-effects models with AR(1) errors based on M-estimation," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(16), pages 8269-8277, August.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:16:p:8269-8277
    DOI: 10.1080/03610926.2016.1177084
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