On the Exact, Asymptotic and Near-exact Distributions for the Likelihood Ratio Statistics to Test Equality of Several Exponential Distributions

Author(s):  
Carlos A. Coelho ◽  
Filipe J. Marques ◽  
Theodore E. Simos ◽  
George Psihoyios ◽  
Ch. Tsitouras ◽  
...  
2016 ◽  
Vol 2016 ◽  
pp. 1-25 ◽  
Author(s):  
Carlos A. Coelho ◽  
Filipe J. Marques ◽  
Sandra Oliveira

The authors address likelihood ratio statistics used to test simultaneously conditions on mean vectors and patterns on covariance matrices. Tests for conditions on mean vectors, assuming or not a given structure for the covariance matrix, are quite common, since they may be easily implemented. But, on the other hand, the practical use of simultaneous tests for conditions on the mean vectors and a given pattern for the covariance matrix is usually hindered by the nonmanageability of the expressions for their exact distribution functions. The authors show the importance of being able to adequately factorize the c.f. of the logarithm of likelihood ratio statistics in order to obtain sharp and highly manageable near-exact distributions, or even the exact distribution in a highly manageable form. The tests considered are the simultaneous tests of equality or nullity of means and circularity, compound symmetry, or sphericity of the covariance matrix. Numerical studies show the high accuracy of the near-exact distributions and their adequacy for cases with very small samples and/or large number of variables. The exact and near-exact quantiles computed show how the common chi-square asymptotic approximation is highly inadequate for situations with small samples or large number of variables.


Author(s):  
Adele A. Mitchell ◽  
Jeannie Tamariz ◽  
Kathleen O‘Connell ◽  
Nubia Ducasse ◽  
Mechthild Prinz ◽  
...  

1997 ◽  
Vol 47 (1-2) ◽  
pp. 59-66 ◽  
Author(s):  
Ming Yin ◽  
Malay Ghosh

A necessary and sufficient condition is given so that the second order probability matching priors based on posterior quantiles will be the priors under which the Bayesian and frequentist Bartlett corrected conditional likelihood ratio statistics differ by 0(l). Several examples are given to illustrate the idea.


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