Filippou, P;
Marra, G;
Radice, R;
(2017)
Penalized likelihood estimation of a trivariate additive probit model.
Biostatistics
, 18
(3)
pp. 569-585.
10.1093/biostatistics/kxx008.
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Abstract
This paper 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.
| Type: | Article |
|---|---|
| Title: | Penalized likelihood estimation of a trivariate additive probit model |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/biostatistics/kxx008 |
| Publisher version: | https://academic.oup.com/biostatistics/article/18/... |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | additive predictor, correlation-based penalty, penalized regression spline, simultaneous parameter estimation, trivariate probit model |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1541226 |
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