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Autor: Lu, Jye-Chyi (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: Gosh, Sujit K. ; Mukhopadhyay, Pabak ; Lu, Jye-Chyi(JC)
Título: Bayesian analysis of zero-inflated regression models
Páginas/Colación: p1360-1375, 16p
Journal of Statistical Planning and Inference vol. 136 no. 4 April 2006
Información de existenciaInformación de existencia

Resumen
In modeling defect counts collected from an established manufacturing processes, there are usually a relatively large number of zeros (non-defects). The commonly used models such as Poisson or Geometric distributions can underestimate the zero-defect probability and hence make it difficult to identify significant covariate effects to improve production quality. This article introduces a flexible class of zero inflated models which includes other familiar models such as the Zero Inflated Poisson (ZIP) models, as special cases. A Bayesian estimation method is developed as an alternative to traditionally used maximum likelihood based methods to analyze such data. Simulation studies show that the proposed method has better finite sample performance than the classical method with tighter interval estimates and better coverage probabilities. A real-life data set is analyzed to illustrate the practicability of the proposed method easily implemented using WinBUGS.

Registro 2 de 2, Base de información BIBCYT
Publicación seriada
Referencias AnalíticasReferencias Analíticas
Autor: Lu, Jye-Chyi ; Chen, Di ; Zhou, Weixing
Título: Quasi-likelihood estimation for GLM with random scales
Páginas/Colación: p401-429, 29p
Journal of Statistical Planning and Inference v. 136 n° 2 February 2006
Información de existenciaInformación de existencia

Resumen
This paper uses random scales similar to random effects used in the generalized linear mixed models to describe “inter-location” population variation in variance components for modeling complicated data obtained from applications such as antenna manufacturing. Our distribution studies lead to a complicated integrated extended quasi-likelihood (IEQL) for parameter estimations and large sample inference derivations. Laplace's expansion and several approximation methods are employed to simplify the IEQL estimation procedures. Asymptotic properties of the approximate IEQL estimates are derived for general structures of the covariance matrix of random scales. Focusing on a few special covariance structures in simpler forms, the authors further simplify IEQL estimates such that typically used software tools such as weighted regression can compute the estimates easily. Moreover, these special cases allow us to derive interesting asymptotic results in much more compact expressions. Finally, numerical simulation results show that IEQL estimates perform very well in several special cases studied.

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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