Mixed Binary-Continuous Copula Regression Models with Application to Adverse Birth Outcomes
Slawa Rokicki () and
No 18-06, CHaRMS Working Papers from Centre for HeAlth Research at the Management School (CHaRMS)
Bivariate copula regression allows for the flexible combination of two arbitrary, continuous marginal distributions with regression effects being placed on potentially all parameters of the resulting bivariate joint response distribution. Motivated by the risk factors for adverse birth outcomes, many of which are dichotomous, we consider mixed binary-continuous responses that extend the bivariate continuous framework to the situation where one response variable is discrete (more precisely binary) while the other response remains continuous. Utilizing the latent continuous representation of binary regression models, we implement a penalized likelihood based approach for the resulting class of copula regression models and employ it in the context of modelling gestational age and the presence/absence of low birth weight. The analysis demonstrates the advantage of the flexible specification of regression impacts including nonlinear effects of continuous covariates and spatial effects. Our results imply that racial and spatial inequalities in the risk factors for infant mortality are even greater than previously suggested.
Keywords: Adverse Birth Outcomes; Copula; Latent Variable; Mixed Discrete-continuous Distributions; Penalised Maximum Likelihood; Penalised Splines (search for similar items in EconPapers)
JEL-codes: I14 C30 I10 (search for similar items in EconPapers)
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