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A Non Parametric Likelihood Ratio Test for Comparison of Several Count Data Model And Its Application to GATS Data

Sujan Rudra, Soma Chowdhry Biswas


A comparison of count data models is obligatory to assess the best performance model. Lack of discrepancy of competing models; it is very important to know which model performs the best based on the particular set of observations. This study proposes to develop a nonparametric likelihood ratio test for the comparison of parametric likelihood multiple models. The proposed multiple (m>2) comparison test particularly will be useful for over-dispersed, mis-specified, nested, non-nested, or overlapping count data models. The proposed test statistic developed based on the Kullback-Leibler Information Criterion (KLIC) and Voung (1989) test for comparing two parametric models, comparing two moment-based models Kitamura (2000), comparing parametric and moment-based models Chen (2007). We affirmed the performance of the proposed test by a Monte Carlo study, and with an example from the GATS (Global Adult Tobacco Survey) data (n=2038). Results show that the effect of competing count data models is not the same propinquity; However, Hurdle Negative Binomial Regression Model is the best-fitted model for the data set.


KLIC, Extension of Voung test, Count Data models, model selection test.

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