Open Access Open Access  Restricted Access Subscription Access

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

Sujan Rudra, Soma Chowdhry Biswas

Abstract



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.

Keywords


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

Full Text:

PDF


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information. Also: DOI is paid service which provided by a third party. We never mentioned that we go for this for our any journal. However, journal have no objection if author go directly for this paid DOI service.