Open Access
Translator Disclaimer
2021 Colombian Women’s Life Patterns: A Multivariate Density Regression Approach
Sara Wade, Raffaella Piccarreta, Andrea Cremaschi, Isadora Antoniano-Villalobos
Bayesian Anal. Advance Publication 1-29 (2021). DOI: 10.1214/20-BA1256

Abstract

Women in Colombia face difficulties related to the patriarchal traits of their societies and well-known conflict afflicting the country since 1948. In this critical context, our aim is to study the relationship between baseline socio-demographic factors and variables associated to fertility, partnership patterns, and work activity. To best exploit the explanatory structure, we propose a Bayesian multivariate density regression model, which can accommodate mixed responses with censored, constrained, and binary traits. The flexible nature of the models allows for nonlinear regression functions and non-standard features in the errors, such as asymmetry or multi-modality. The model has interpretable covariate-dependent weights constructed through normalization, allowing for combinations of categorical and continuous covariates. Computational difficulties for inference are overcome through an adaptive truncation algorithm combining adaptive Metropolis-Hastings and sequential Monte Carlo to create a sequence of automatically truncated posterior mixtures. For our study on Colombian women’s life patterns, a variety of quantities are visualised and described, and in particular, our findings highlight the detrimental impact of family violence on women’s choices and behaviors.

Citation

Download Citation

Sara Wade. Raffaella Piccarreta. Andrea Cremaschi. Isadora Antoniano-Villalobos. "Colombian Women’s Life Patterns: A Multivariate Density Regression Approach." Bayesian Anal. Advance Publication 1 - 29, 2021. https://doi.org/10.1214/20-BA1256

Information

Published: 2021
First available in Project Euclid: 12 January 2021

Digital Object Identifier: 10.1214/20-BA1256

Subjects:
Primary: 62G07, 62G08
Secondary: 62N01, 62P25

JOURNAL ARTICLE
29 PAGES


SHARE
Advance Publication
Back to Top