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Mixed Binary-Continuous Copula Regression Models with Application to Adverse Birth Outcomes

Klein, N., Kneib, T., Marra, G. , Radice, R., Rokicki, S. R. & McGovern, M. (2019). Mixed Binary-Continuous Copula Regression Models with Application to Adverse Birth Outcomes. Statistics in Medicine, 38(3), pp. 413-436. doi: 10.1002/sim.7985


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 a study examining the risk factors of adverse birth outcomes, we consider mixed binary-continuous responses that extend this 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 jointly gestational age and the presence/absence of low birth weight. The analysis strongly benefits from the flexible specification of regression effects 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.

Publication Type: Article
Additional Information: This is the peer reviewed version of the following article: Klein N, Kneib T, Marra G, Radice R, Rokicki S, McGovern ME. Mixed binary‐continuous copula regression models with application to adverse birth outcomes. Statistics in Medicine. 2019;38:413–436., which has been published in final form at This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
Publisher Keywords: Adverse birth outcomes; Copula; Latent variable; Mixed discrete-continuous distributions; Penalised maximum likelihood; Penalised splines.
Subjects: H Social Sciences > HA Statistics
R Medicine > RG Gynecology and obstetrics
Departments: Bayes Business School > Actuarial Science & Insurance
SWORD Depositor:
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