Partially Linear Spatial Probit Models
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
Partially Linear Spatial Probit Models
Author(s) :
AHMED, Mohamed-Salem [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Dabo-Niang, Sophie [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Genin, Michaël [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Hassan, Alaa Ali [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Dabo-Niang, Sophie [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Genin, Michaël [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Hassan, Alaa Ali [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Journal title :
Annales de l'ISUP
Pages :
71-96
Publisher :
Publications de l’Institut de Statistique de l’Université de Paris
Publication date :
2019
ISSN :
1626-1607
HAL domain(s) :
Statistiques [stat]/Méthodologie [stat.ME]
English abstract : [en]
A partially linear probit model for spatially dependent data is considered. A triangular array setting is used to cover various patterns of spatial data. Conditional spatial heteroscedasticity and non-identically distributed ...
Show more >A partially linear probit model for spatially dependent data is considered. A triangular array setting is used to cover various patterns of spatial data. Conditional spatial heteroscedasticity and non-identically distributed observations and a linear process for disturbances are assumed, allowing various spatial dependencies. The estimation procedure is a combination of a weighted likelihood and a generalized method of moments. The procedure first fixes the parametric components of the model and then estimates the non-parametric part using weighted likelihood; the obtained estimate is then used to construct a GMM parametric component estimate. The consistency and asymptotic distribution of the estimators are established under sufficient conditions. Some simulation experiments are provided to investigate the finite sample performance of the estimators.Show less >
Show more >A partially linear probit model for spatially dependent data is considered. A triangular array setting is used to cover various patterns of spatial data. Conditional spatial heteroscedasticity and non-identically distributed observations and a linear process for disturbances are assumed, allowing various spatial dependencies. The estimation procedure is a combination of a weighted likelihood and a generalized method of moments. The procedure first fixes the parametric components of the model and then estimates the non-parametric part using weighted likelihood; the obtained estimate is then used to construct a GMM parametric component estimate. The consistency and asymptotic distribution of the estimators are established under sufficient conditions. Some simulation experiments are provided to investigate the finite sample performance of the estimators.Show less >
Language :
Anglais
Popular science :
Non
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