Inicio Nosotros Búsquedas
Buscar en nuestra Base de Datos:     
Autor: Ghosh, Malay (Comienzo)
2 registros cumplieron la condición especificada en la base de información BIBCYT. ()
Registro 1 de 2, Base de información BIBCYT
Publicación seriada
Referencias AnalíticasReferencias Analíticas
Autor: Ghosh, Malay ; Sinha, Karabi
Título: Empirical Bayes estimation in finite population sampling under functional measurement error models
Páginas/Colación: p2759-2773
Journal of Statistical Planning and Inference Vol. 137, no. 9 September 2007
Información de existenciaInformación de existencia

Resumen
The paper considers simultaneous estimation of finite population means for several strata. A model-based approach is taken, where the covariates in the super-population model are subject to measurement errors. Empirical Bayes (EB) estimators of the strata means are developed and an asymptotic expression for the MSE of the EB estimators is provided. It is shown that the proposed EB estimators are “first order optimal” in the sense of Robbins [1956. An empirical Bayes approach to statistics. In: Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, University of California Press, Berkeley, pp. 157–164], while the regular EB estimators which ignore the measurement error are not.

Registro 2 de 2, Base de información BIBCYT
Publicación seriada
Referencias AnalíticasReferencias Analíticas
Autor: Ghosh, Malay ; Maiti, Tapabrata ; Kim , Dalho ; Chakraborty, Sounak ; Tewari, Ashutosh
Título: Hierarchical Bayesian Neural Networks: An Application to a Prostate Cancer Study
Páginas/Colación: pp. 601 - 608
Url: Ir a http://oberon.asa.catchword.org/vl=5518955/cl=25/nw=1/rpsv/cw/asa/01621459/v99n467/s7/p601http://oberon.asa.catchword.org/vl=5518955/cl=25/nw=1/rpsv/cw/asa/01621459/v99n467/s7/p601
Journal of the American Statistical Association Vol. 99, no. 467 September 2004
Información de existenciaInformación de existencia

Resumen

 

Prostate cancer is one of the most common cancers in American men. Management depends on the staging of prostate cancer. Only cancers that are confined to organs of origin are potentially curable. The article considers a hierarchical Bayesian neural network approach for posterior prediction probabilities of certain features indicative of non-organ-confined prostate cancer. The Bayesian procedure is implemented by an application of the Markov chain Monte Carlo numerical integration technique. For the problem at hand, the hierarchical Bayesian neural network approach is shown to be superior to the approach based on hierarchical Bayesian logistic regression model as well as the classical feedforward neural networks.

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

UCLA - Biblioteca de Ciencias y Tecnologia Felix Morales Bueno

Generados por el servidor 'bibcyt.ucla.edu.ve' (18.190.176.78)
Adaptive Server Anywhere (07.00.0000)
ODBC
Sesión="" Sesión anterior=""
 ejecutando Back-end Alejandría BE 7.0.7b0 ** * *
18.190.176.78 (NTM) bajo el ambiente Apache/2.2.4 (Win32) PHP/5.2.2.
usando una conexión ODBC (RowCount) al manejador de bases de datos..
Versión de la base de información BIBCYT: 7.0.0 (con listas invertidas [2.0])

Cliente: 18.190.176.78
Salida con Javascript
** Back-end Alejandría BE 7.0.7b0 *