Penalized likelihood estimation of a trivariate additive probit model.
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.
Department of Statistical Science, University College London, Gower Street, London WC1E 6BT, UK.
Panagiota
P
Filippou
Department of Statistical Science, University College London, Gower Street, London WC1E 6BT, UK.
Giampiero
G
Marra
Department of Economics, Mathematics and Statistics, Birkbeck, University of London, Malet Street, London WC1E 7HX, UK.
Rosalba
R
Radice
D000465:Algorithms; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D009657:North Carolina; D011247:Pregnancy; D011256:Pregnancy Outcome
Additive predictor; Correlation-based penalty; Penalized regression spline; Simultaneous parameter estimation; Trivariate probit model