Mohamad ALNAKAWA, Neslihan İYİT
In this study, using the "Multiple Indicators Cluster Survey 5" (MICS5) conducted by UNICEF in 2014, Republic of the Congo in the level of lower middle-income countries is taken to identify the risk factors affecting “child mortality under-5” by using Poisson regression model, negative binomial (NB2) regression model, zero-inflated Poisson (ZIP) regression model and zero-inflated negative binomial (ZINB) regression model in the class of generalized linear models (GLMs). For this purpose, education (as attended to a school or not attended to a school), wealth index quintile (as poorest, second, middle, fourth and richest population wealth quintiles), area of residence (as rural or urban), mother's age (as 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49 years), breastfeeding status (as received breast milk or not received breast milk) and pregnancy method (as used any methods to avoid pregnancy or not) are taken as categorical independent variables as possible risk factors affecting “child mortality under-5” in the Republic of the Congo. By using R-programme to statistically analyze the child mortality under-5 data of this country, among the above-mentioned regression models and zero-inflated regression models, “ZIP regression model” is found as the best model according to the information criteria (IC) as AIC, BIC, AICc and also log-likelihood values. Statistical interpretations and conclusions will be done according to this type of zero-inflated regression model for the Republic of the Congo “child mortality under-5” data. Acknowledgement: This study is a part of M.Sc. Thesis titled “An Application Based on Zero-Inflated Poisson and Negative Binomial Regression Models” supervised by Assoc.Prof.Dr. Neslihan İYİT continuing at Selcuk University, Graduate School of Natural Sciences, Statistics Department.

Anahtar Kelimeler: Child Mortality Under-5, Generalized Linear Model, Poisson Regression Model, Negative Binomial Regression Model, Zero-Inflated Poisson Regression Model