An empirical likelihood ratio based goodness-of-fit test for Inverse Gaussian distributions

2011 ◽  
Vol 141 (6) ◽  
pp. 2128-2140 ◽  
Author(s):  
Albert Vexler ◽  
Guogen Shan ◽  
Seongeun Kim ◽  
Wan-Min Tsai ◽  
Lili Tian ◽  
...  
Keyword(s):  
Likelihood Ratio ◽  
Empirical Likelihood ◽  
Goodness Of Fit ◽  
Inverse Gaussian ◽  
Goodness Of Fit Test ◽  
Gaussian Distributions ◽  
Empirical Likelihood Ratio

scholarly journals A Simple Empirical Likelihood Ratio Test for Normality Based on the Moment Constraints of a Half-Normal Distribution

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
C. S. Marange ◽  
Y. Qin
Keyword(s):  
Normal Distribution ◽  
Likelihood Ratio ◽  
Empirical Likelihood ◽  
Goodness Of Fit ◽  
Real Data ◽  
Ratio Test ◽  
Empirical Likelihood Ratio ◽  
Empirical Likelihood Ratio Test ◽  
The Moment ◽  
Test For Normality

A simple and efficient empirical likelihood ratio (ELR) test for normality based on moment constraints of the half-normal distribution was developed. The proposed test can also be easily modified to test for departures from half-normality and is relatively simple to implement in various statistical packages with no ordering of observations required. Using Monte Carlo simulations, our test proved to be superior to other well-known existing goodness-of-fit (GoF) tests considered under symmetric alternative distributions for small to moderate sample sizes. A real data example revealed the robustness and applicability of the proposed test as well as its superiority in power over other common existing tests studied.

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