Penalized likelihood estimation of a trivariate additive probit model.
Biostatistics
; 18(3): 569-585, 2017 Jul 01.
Article
en En
| MEDLINE
| ID: mdl-28334261
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.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Funciones de Verosimilitud
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Female
/
Humans
/
Pregnancy
País/Región como asunto:
America do norte
Idioma:
En
Revista:
Biostatistics
Año:
2017
Tipo del documento:
Article
País de afiliación:
Reino Unido