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

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

SUBMITTER: Kareem YO 

PROVIDER: S-EPMC6921497 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature