Influence analysis for Poisson inverse Gaussian regression models based on the EM algorithm

Metrika ◽  
2007 ◽  
Vol 67 (1) ◽  
pp. 49-62 ◽  
Author(s):  
Feng-Chang Xie ◽  
Bo-Cheng Wei
Keyword(s):  
Em Algorithm ◽  
Regression Models ◽  
Inverse Gaussian ◽  
Influence Analysis ◽  
The Em Algorithm ◽  
Gaussian Regression

scholarly journals Modelling Inverse Gaussian Data with Censored Response Values: EM versus MCMC

2011 ◽  
Vol 2011 ◽  
pp. 1-8 ◽  
Author(s):  
R. S. Sparks ◽  
G. Sutton ◽  
P. Toscas ◽  
J. T. Ormerod
Keyword(s):  
Em Algorithm ◽  
Environmental Variables ◽  
Detection Limits ◽  
Inverse Gaussian ◽  
Building Models ◽  
The Em Algorithm ◽  
High Detection ◽  
Using Data ◽  
Censored Response ◽  
Gaussian Data

Low detection limits are common in measure environmental variables. Building models using data containing low or high detection limits without adjusting for the censoring produces biased models. This paper offers approaches to estimate an inverse Gaussian distribution when some of the data used are censored because of low or high detection limits. Adjustments for the censoring can be made if there is between 2% and 20% censoring using either the EM algorithm or MCMC. This paper compares these approaches.

POWER FUNCTION FOR INVERSE GAUSSIAN REGRESSION MODELS

2001 ◽  
Vol 30 (5) ◽  
pp. 787-797 ◽  
Author(s):  
Mammo Woldie ◽  
J. Leroy Folks ◽  
John P. Chandler
Keyword(s):  
Power Function ◽  
Regression Models ◽  
Inverse Gaussian ◽  
Gaussian Regression

Varying Dispersion Diagnostics for Inverse Gaussian Regression Models

2004 ◽  
Vol 31 (10) ◽  
pp. 1157-1170 ◽  
Author(s):  
Jin-Guan Lin ◽  
Bo-Cheng Wei ◽  
Nan-Song Zhang
Keyword(s):  
Regression Models ◽  
Inverse Gaussian ◽  
Varying Dispersion ◽  
Gaussian Regression
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