Weighted Bayesian Poisson Regression for The Number of Children Ever Born per Woman in Bangladesh

Jabed H Tomal; Jahidur Rahman Khan; Abdus S Wahed

doi:10.1007/s44199-022-00044-2

Weighted Bayesian Poisson Regression for The Number of Children Ever Born per Woman in Bangladesh

J Stat Theory Appl. 2022;21(3):79-105. doi: 10.1007/s44199-022-00044-2. Epub 2022 Jun 14.

Authors

Jabed H Tomal¹, Jahidur Rahman Khan², Abdus S Wahed³

Affiliations

¹ Department of Mathematics and Statistics, Thompson Rivers University, Kamloops, British Columbia Canada.
² Discipline of Paediatrics, School of Clinical Medicine, University of New South Wales, Sydney, Australia.
³ Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pennsylvania, USA.

Abstract

Number of children ever born to women of reproductive age forms a core component of fertility and is vital to the population dynamics in any country. Using Bangladesh Multiple Indicator Cluster Survey 2019 data, we fitted a novel weighted Bayesian Poisson regression model to identify multi-level individual, household, regional and societal factors of the number of children ever born among married women of reproductive age in Bangladesh. We explored the robustness of our results using multiple prior distributions, and presented the Metropolis algorithm for posterior realizations. The method is compared with regular Bayesian Poisson regression model using a Weighted Bayesian Information Criterion. Factors identified emphasize the need to revisit and strengthen the existing fertility-reduction programs and policies in Bangladesh.

Supplementary information: The online version contains supplementary material available at 10.1007/s44199-022-00044-2.

Keywords: Bangladesh; Bayesian method; Fertility rate; Poisson regression; Weighted likelihood.