id: 06083015
dt: j
an: 06083015
au: Chen, Xue-Dong; Fu, Ying-Zi
ti: Model selection for zero-inflated regression with missing covariates.
so: Comput. Stat. Data Anal. 55, No. 1, 765-773 (2011).
py: 2011
pu: Elsevier Science B.V. (North-Holland), Amsterdam
la: EN
cc: 
ut: zero-inflation; missing data; AIC; EM algorithm
ci: 
li: doi:10.1016/j.csda.2010.06.023
ab: Summary: Count data are widely existing in the fields of medical trials,
    public health, surveys and environmental studies. In analyzing count
    data, it is important to find out whether 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 the 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.
rv: