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Model selection for zero-inflated regression with missing covariates

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  • Chen, Xue-Dong
  • Fu, Ying-Zi

Abstract

Count data are widely existed in the fields of medical trials, public health, surveys and environmental studies. In analyzing count data, it is important to find out whether the zero-inflation exists or not and how to select the most suitable model. However, the classic AIC criterion for model selection is invalid when the observations are missing. In this paper, we develop a new model selection criterion in line with AIC for the zero-inflated regression models with missing covariates. This method is a modified version of Monte Carlo EM algorithm which is based on the data augmentation scheme. One of the main attractions of this new method is that it is applicable for comparison of candidate models regardless of whether there are missing data or not. What is more, it is very simple to compute as it is just a by-product of Monte Carlo EM algorithm when the estimations of parameters are obtained. A simulation study and a real example are used to illustrate the proposed methodologies.

Suggested Citation

  • Chen, Xue-Dong & Fu, Ying-Zi, 2011. "Model selection for zero-inflated regression with missing covariates," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 765-773, January.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:1:p:765-773
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    References listed on IDEAS

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    1. Joseph G. Ibrahim & Ming-Hui Chen & Stuart R. Lipsitz, 1999. "Monte Carlo EM for Missing Covariates in Parametric Regression Models," Biometrics, The International Biometric Society, vol. 55(2), pages 591-596, June.
    2. Lan Huang & Ming-Hui Chen & Joseph G. Ibrahim, 2005. "Bayesian Analysis for Generalized Linear Models with Nonignorably Missing Covariates," Biometrics, The International Biometric Society, vol. 61(3), pages 767-780, September.
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    6. Angers, Jean-Francois & Biswas, Atanu, 2003. "A Bayesian analysis of zero-inflated generalized Poisson model," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 37-46, February.
    7. Qingxia Chen & Joseph G. Ibrahim, 2006. "Semiparametric Models for Missing Covariate and Response Data in Regression Models," Biometrics, The International Biometric Society, vol. 62(1), pages 177-184, March.
    8. Gerda Claeskens & Fabrizio Consentino, 2008. "Variable Selection with Incomplete Covariate Data," Biometrics, The International Biometric Society, vol. 64(4), pages 1062-1069, December.
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    Cited by:

    1. Lukusa, Martin T. & Phoa, Frederick Kin Hing, 2020. "A note on the weighting-type estimations of the zero-inflated Poisson regression model with missing data in covariates," Statistics & Probability Letters, Elsevier, vol. 158(C).
    2. Antonio J. Sáez-Castillo & Antonio Conde-Sánchez, 2017. "Detecting over- and under-dispersion in zero inflated data with the hyper-Poisson regression model," Statistical Papers, Springer, vol. 58(1), pages 19-33, March.
    3. Shen-Ming Lee & T. Martin Lukusa & Chin-Shang Li, 2020. "Estimation of a zero-inflated Poisson regression model with missing covariates via nonparametric multiple imputation methods," Computational Statistics, Springer, vol. 35(2), pages 725-754, June.
    4. Yang, Miao & Das, Kalyan & Majumdar, Anandamayee, 2016. "Analysis of bivariate zero inflated count data with missing responses," Journal of Multivariate Analysis, Elsevier, vol. 148(C), pages 73-82.
    5. Augustin, Nicole H. & Sauleau, Erik-André & Wood, Simon N., 2012. "On quantile quantile plots for generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2404-2409.
    6. T. Martin Lukusa & Shen-Ming Lee & Chin-Shang Li, 2016. "Semiparametric estimation of a zero-inflated Poisson regression model with missing covariates," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(4), pages 457-483, May.

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