Bayesian Inference for Inverse Gaussian Data with Emphasis on the Coefficient of Variation

2021 ◽  
pp. 79-96
Author(s):  
Yogendra P. Chaubey ◽  
Murari Singh ◽  
Debaraj Sen
Keyword(s):  
Bayesian Inference ◽  
Coefficient Of Variation ◽  
Inverse Gaussian ◽  
Gaussian Data

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.

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