Bayesian Finite Mixture Negative Binomial Model for Over-dispersed Count Data with Application to DMFT Index Data
International Journal of Data Science and Analysis
Volume 5, Issue 5, October 2019, Pages: 104-110
Received: Oct. 8, 2019;
Accepted: Oct. 23, 2019;
Published: Oct. 30, 2019
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Kipngetich Gideon, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Anthony Wanjoya, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Samuel Mwalili, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
To establish viable statistical model for modelling and analyzing DMFT index data which is important in oral health studies, difficulty arise when DMFT index data is characterized by over-dispersion. Over-dispersion caused by unobserved heterogeneity in the data pose a problem in fitting more common models to this data. and failure to account on such heterogeneity in the model can undermine the validity of the empirical results. The limitations of other count data models to account for overdispersion in DMFT index data due to existence of heterogeneity in the data, this paper formulated alternative model that captures heterogeneity in the data, that is Bayesian Finite mixture negative binomial regression model and the model applied to simulated overdispersed count data to determine the exact number of negative binomial components to be mixed and finally apply the model to DMFT index data. Bayesian finite mixture Negative Binomial (BFMNB-3) regression model is useful since the data were collected from heterogenous population. simulation results shows that 3-component Bayesian finite mixture of NB regression model converges and was quite enough to model the overdispersed simulated count data, applying BFMNB-3 model to DMFT index data, the model capability to capture heterogeneity in the data identifies that the methods; all the treatment (all methods together), mouth wash with 0.2% sodium fluoride and Oral hygiene were the best methods in preventing tooth decay in children in Belo Horizonte (Brazil) aged seven years this shows that BFMNB-3 performs better than BNB model were due to heterogeneity present in methods it only identifies methods; all the treatment (all methods together) and mouth wash with 0.2% sodium fluoride to be the best methods for preventing tooth decay for children in Belo Horizonte (Brazil) aged seven while this two methods were not the only significant methods, therefore from results there is complete superiority of BFMNB-3 over BNB model. R statistical software was used to accomplish the objectives of this paper.
Bayesian Finite Mixture Negative Binomial Model for Over-dispersed Count Data with Application to DMFT Index Data, International Journal of Data Science and Analysis.
Vol. 5, No. 5,
2019, pp. 104-110.
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