Open Access
June 2022 Colombian Women’s Life Patterns: A Multivariate Density Regression Approach
Sara Wade, Raffaella Piccarreta, Andrea Cremaschi, Isadora Antoniano-Villalobos
Bayesian Anal. 17(2): 405-433 (June 2022). DOI: 10.1214/20-BA1256


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.


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Sara Wade. Raffaella Piccarreta. Andrea Cremaschi. Isadora Antoniano-Villalobos. "Colombian Women’s Life Patterns: A Multivariate Density Regression Approach." Bayesian Anal. 17 (2) 405 - 433, June 2022.


Published: June 2022
First available in Project Euclid: 12 January 2021

MathSciNet: MR4483225
Digital Object Identifier: 10.1214/20-BA1256

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

Keywords: adaptive truncation , Bayesian nonparametrics , non-informative censoring , sequential Monte Carlo , time-to-event

Vol.17 • No. 2 • June 2022
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