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Inferences in semi-parametric dynamic mixed models for longitudinal count data

Author

Listed:
  • Nan Zheng

    (Memorial University)

  • Brajendra C. Sutradhar

    (Memorial University)

Abstract

This paper considers a semi-parametric mixed model for longitudinal counts under the assumption that for conditional on a common random effect over time the repeated count responses of an individual follow a Poisson AR(1) (auto-regressive order 1) non-stationary correlation structure. A step-by-step estimation approach is developed which provides consistent estimators for the non-parametric function, regression parameters, variance of the random effects, and auto-correlation structure of the model. Proofs for the consistency properties of the estimators along with their convergence rates are derived. A simulation study is conducted to examine first the estimation effects on parameters when the non-parametric function is ignored, and then an overall estimation study is carried out in the presence of the non-parametric function by including its estimation as well.

Suggested Citation

  • Nan Zheng & Brajendra C. Sutradhar, 2018. "Inferences in semi-parametric dynamic mixed models for longitudinal count data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(1), pages 215-247, February.
    • Handle: RePEc:spr:aistmt:v:70:y:2018:i:1:d:10.1007_s10463-016-0590-9
    DOI: 10.1007/s10463-016-0590-9
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    References listed on IDEAS

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