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Semiparametric analysis of longitudinal zero-inflated count data

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  • Feng, Jiarui
  • Zhu, Zhongyi

Abstract

In this article, we consider a semiparametric zero-inflated Poisson mixed model that postulates a possible nonlinear relationship between the natural logarithm of the mean of the counts and a particular covariate in the longitudinal studies. A penalized log-likelihood function is proposed and Monte Carlo expectation-maximization algorithm is used to derive the estimates. Under some mild conditions, we establish the consistency and asymptotic normality of the resulting estimators. Simulation studies are carried out to investigate the finite sample performance of the proposed method. For illustration purposes, the method is applied to a data set from a pharmaceutical company where the variable of interest is the number of episodes of side effects after the patient has taken the treatments.

Suggested Citation

  • Feng, Jiarui & Zhu, Zhongyi, 2011. "Semiparametric analysis of longitudinal zero-inflated count data," Journal of Multivariate Analysis, Elsevier, vol. 102(1), pages 61-72, January.
    • Handle: RePEc:eee:jmvana:v:102:y:2011:i:1:p:61-72
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    References listed on IDEAS

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    1. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, October.
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    6. Guo You Qin & Zhong Yi Zhu, 2009. "Robustified Maximum Likelihood Estimation in Generalized Partial Linear Mixed Model for Longitudinal Data," Biometrics, The International Biometric Society, vol. 65(1), pages 52-59, March.
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