IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v55y2011i1p765-773.html

Model selection for zero-inflated regression with missing covariates

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(10)00268-9
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Gerda Claeskens & Fabrizio Consentino, 2008. "Variable Selection with Incomplete Covariate Data," Biometrics, The International Biometric Society, vol. 64(4), pages 1062-1069, December.
    2. Joseph G. Ibrahim & Ming-Hui Chen & Stuart R. Lipsitz & Amy H. Herring, 2005. "Missing-Data Methods for Generalized Linear Models: A Comparative Review," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 332-346, March.
    3. 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.
    4. Sik-Yum Lee & Xin-Yuan Song, 2004. "Maximum Likelihood Analysis of a General Latent Variable Model with Hierarchically Mixed Data," Biometrics, The International Biometric Society, vol. 60(3), pages 624-636, September.
    5. 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.
    6. 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.
    7. 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.
    8. W. R. Gilks & P. Wild, 1992. "Adaptive Rejection Sampling for Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 337-348, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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).
    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. 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.
    6. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiang, Wei & Josse, Julie & Lavielle, Marc, 2020. "Logistic regression with missing covariates—Parameter estimation, model selection and prediction within a joint-modeling framework," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    2. Chen, Qingxia & Ibrahim, Joseph G. & Chen, Ming-Hui & Senchaudhuri, Pralay, 2008. "Theory and inference for regression models with missing responses and covariates," Journal of Multivariate Analysis, Elsevier, vol. 99(6), pages 1302-1331, July.
    3. Joseph Ibrahim & Geert Molenberghs, 2009. "Missing data methods in longitudinal studies: a review," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(1), pages 1-43, May.
    4. Nanhua Zhang & Roderick J. Little, 2012. "A Pseudo-Bayesian Shrinkage Approach to Regression with Missing Covariates," Biometrics, The International Biometric Society, vol. 68(3), pages 933-942, September.
    5. Susanne Gschlößl & Claudia Czado, 2008. "Modelling count data with overdispersion and spatial effects," Statistical Papers, Springer, vol. 49(3), pages 531-552, July.
    6. Joseph G. Ibrahim & Hongtu Zhu & Ramon I. Garcia & Ruixin Guo, 2011. "Fixed and Random Effects Selection in Mixed Effects Models," Biometrics, The International Biometric Society, vol. 67(2), pages 495-503, June.
    7. Lei Jin & Suojin Wang, 2010. "A Model Validation Procedure when Covariate Data are Missing at Random," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(3), pages 403-421, September.
    8. Fang, Fang & Shao, Jun, 2016. "Iterated imputation estimation for generalized linear models with missing response and covariate values," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 111-123.
    9. Poonam Malakar & Sudhir Paul & Abdulla Mamun & Subhamoy Pal, 2025. "Effect of Missing Responses on the $$C(\alpha )$$ C ( α ) or Score Tests in One-way Layout of Count Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 87(1), pages 147-172, May.
    10. Hongtu Zhu & Joseph G. Ibrahim & Xiaoyan Shi, 2009. "Diagnostic Measures for Generalized Linear Models with Missing Covariates," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(4), pages 686-712, December.
    11. Liang, Hua, 2008. "Generalized partially linear models with missing covariates," Journal of Multivariate Analysis, Elsevier, vol. 99(5), pages 880-895, May.
    12. Ming‐Hui Chen & Joseph G. Ibrahim, 2001. "Maximum Likelihood Methods for Cure Rate Models with Missing Covariates," Biometrics, The International Biometric Society, vol. 57(1), pages 43-52, March.
    13. Yang Zhao, 2021. "Semiparametric model for regression analysis with nonmonotone missing data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(2), pages 461-475, June.
    14. Lyubov Doroshenko & Brunero Liseo, 2023. "Generalized linear mixed model with bayesian rank likelihood," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(2), pages 425-446, June.
    15. Lee, Min Cherng & Mitra, Robin, 2016. "Multiply imputing missing values in data sets with mixed measurement scales using a sequence of generalised linear models," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 24-38.
    16. Tamara Broderick & Robert Gramacy, 2011. "Classification and Categorical Inputs with Treed Gaussian Process Models," Journal of Classification, Springer;The Classification Society, vol. 28(2), pages 244-270, July.
    17. Pang, W. K. & Yang, Z. H. & Hou, S. H. & Leung, P. K., 2002. "Non-uniform random variate generation by the vertical strip method," European Journal of Operational Research, Elsevier, vol. 142(3), pages 595-609, November.
    18. Roy, Vivekananda, 2014. "Efficient estimation of the link function parameter in a robust Bayesian binary regression model," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 87-102.
    19. Sik-Yum Lee & Xin-Yuan Song, 2007. "A Unified Maximum Likelihood Approach for Analyzing Structural Equation Models With Missing Nonstandard Data," Sociological Methods & Research, , vol. 35(3), pages 352-381, February.
    20. Lee, Sik-Yum & Song, Xin-Yuan, 2008. "On Bayesian estimation and model comparison of an integrated structural equation model," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4814-4827, June.

    More about this item

    Keywords

    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:55:y:2011:i:1:p:765-773. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.