City Research Online

Abstract

This article proposes a penalized likelihood method to estimate a trivariate probit model, which accounts for several types of covariate effects (such as linear, nonlinear, random, and spatial effects), as well as error correlations. The proposed approach also addresses the difficulty in estimating accurately the correlation coefficients, which characterize the dependence of binary responses conditional on covariates. The parameters of the model are estimated within a penalized likelihood framework based on a carefully structured trust region algorithm with integrated automatic multiple smoothing parameter selection. The relevant numerical computation can be easily carried out using the SemiParTRIV() function in a freely available R package. The proposed method is illustrated through a case study whose aim is to model jointly adverse birth binary outcomes in North Carolina.

Publication Type:	Article
Additional Information:	This is a pre-copyedited, author-produced version of an article accepted for publication in 'Biostatistics' following peer review. The version of record Filippou, P., Marra, G. and Radice, R. (2017). Penalized likelihood estimation of a trivariate additive probit model. Biostatistics, 18(3), pp. 569-585. is available online at:https://doi.org/10.1093/biostatistics/kxx008.
Publisher Keywords:	Additive predictor; Correlation-based penalty; Penalized regression spline; Simultaneous parameter estimation; Trivariate probit model
Subjects:	H Social Sciences > HA Statistics Q Science > QA Mathematics Q Science > QH Natural history > QH301 Biology
Departments:	Bayes Business School > Actuarial Science & Insurance
SWORD Depositor:	Symplectic Administrator

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