Robustness of zero-augmented models over generalized linear models in analysing fertility data in Nigeria

Abstract

Abstract Objective Fertility is a count data usually rightly skewed and exhibiting large number of zeros than the distributional assumption of the generalized linear models (GLMs). This study examined the robustness of zero-augmented models over GLMs to fit fertility data across regions in Nigeria. The 2013 Nigeria Demographic and Health Survey data were used. The fertility models fitted included: Poisson, negative binomial, zero-inflated Poisson, zero-inflated negative binomial, hurdle Poisson and hurdle negative binomial. Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) were used to identify the model with best fit (α = 0.05). Results The percentage of zero count in the fertility responses were 21.3, 23.9, 31.1, 30.7, 37.6 and 42.4 in North West, North East, North Central, South West, South South and South East regions respectively. In all the six regions in Nigeria, the zero-augmented models were better than the generalized linear models except for North Central. Extensively, the zero-augmented negative binomial based models were of better fit than their Poisson based counterparts; or in rare cases maybe indistinguishable. However, specific family of zero-augmented model is recommended for each region in Nigeria.

Keywords

• Zero-augmented models
• Fertility
• Best fit model
• Nigeria