Shrinkage testimation of the shape parameter of an inverse Gaussian distribution using a (α + β)min test

1997 ◽  
Vol 37 (5) ◽  
pp. 825-827
Author(s):  
B.N. Pandey ◽  
Omkar Rai
Keyword(s):  
Gaussian Distribution ◽  
Shape Parameter ◽  
Inverse Gaussian Distribution ◽  
Inverse Gaussian

scholarly journals Designing of Repetitive Group Sampling Plan under the Inverse Gaussian Distribution

2019 ◽  
Vol 9 (1S4) ◽  
pp. 987-991
Keyword(s):  
Gaussian Distribution ◽  
Shape Parameter ◽  
Gaussian Model ◽  
Inverse Gaussian Distribution ◽  
Design Parameters ◽  
Inverse Gaussian ◽  
Shape Parameters ◽  
Sampling Plans ◽  
Truncated Life Test ◽  
Repetitive Group Sampling

This study investigates the attributes repetitive group sampling plans based on a truncated life test under the inverse Gaussian distribution with known shape parameter. The sample size and two acceptance numbers are the three design parameters determined for the proposed repetitive group sampling plans for different mean ratios. Tables are constructed to determine the optimal design parameters for different values of shape parameters of the inverse Gaussian model and the results are explained by with examples. Also the effect of misspecification of shape parameters is also discussed.

scholarly journals Statistical Analysis of Location Parameter of Inverse Gaussian Distribution Under Noninformative Priors

2019 ◽  
Vol 3 (2) ◽  
pp. 62-76
Author(s):  
Nida Khan ◽  
Muhammad Aslam
Keyword(s):  
Gaussian Distribution ◽  
Shape Parameter ◽  
Real Life ◽  
Location Parameter ◽  
Inverse Gaussian Distribution ◽  
Inverse Gaussian ◽  
Noninformative Priors ◽  
Life Data ◽  
Squared Error ◽  
Real Life Data

Bayesian estimation for location parameter of the inverse Gaussian distribution is presented in this paper. Noninformative priors (Uniform and Jeffreys) are assumed to be the prior distributions for the location parameter as the shape parameter of the distribution is considered to be known. Four loss functions: Squared error, Trigonometric, Squared logarithmic and Linex are used for estimation. Bayes risks are obtained to find the best Bayes estimator through simulation study and real life data

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