On Symmetrizing Transformation of the Sample Coefficient of Variation from a Normal Population

2013 ◽  
Vol 42 (9) ◽  
pp. 2118-2134 ◽  
Author(s):  
Yogendra P. Chaubey ◽  
Murari Singh ◽  
Debaraj Sen
Keyword(s):  
Coefficient Of Variation ◽  
Normal Population ◽  
Sample Coefficient Of Variation

A Note on Test for Coefficient of Variation

1989 ◽  
Vol 38 (3-4) ◽  
pp. 225-230 ◽  
Author(s):  
K. Aruna Rao ◽  
A.R.S. Bhatta
Keyword(s):  
Coefficient Of Variation ◽  
Normal Approximation ◽  
Alternative Hypothesis ◽  
Normal Population ◽  
Large Sample ◽  
Sample Test ◽  
Hypo Thesis ◽  
Level Of Significance ◽  
Null Hypo ◽  
Sample Coefficient Of Variation

In this paper we deal with large sample test for coefficient of variation when the observations come from a normal population. Such large sample tests are based on normal approximation for sample coefficient of variation. When the sa mple size is small or moderate, the normal approximation may not be accurate in the sense that the attained level of the test Is not close to the nominal level. Following Bhattacharya and Ghosh (1978), we obtain valid Edgeworth expansion for sample coefficient of variation to O(n-1) under simple null hypo thesis and contiguous alternative hypothesis. This helps one to determine the critical region such that attained level of significance is closer to the normal level and the power function is more accurate.

On the percentage points of the sample coefficient of variation

Biometrika ◽  
1968 ◽  
Vol 55 (3) ◽  
pp. 580-581 ◽  
Author(s):  
BORIS IGLEWICZ ◽  
RAYMOND H. MYERS ◽  
RICHARD B. HOWE
Keyword(s):  
Coefficient Of Variation ◽  
Percentage Points ◽  
Sample Coefficient Of Variation

FINITE-SAMPLE MOMENTS OF THE COEFFICIENT OF VARIATION

2009 ◽  
Vol 25 (1) ◽  
pp. 291-297 ◽  
Author(s):  
Yong Bao
Keyword(s):  
Coefficient Of Variation ◽  
Quadratic Forms ◽  
Mean Squared Error ◽  
General Distribution ◽  
Finite Sample ◽  
Sample Bias ◽  
Squared Error ◽  
Finite Sample Bias ◽  
Sample Coefficient Of Variation

We study the finite-sample bias and mean squared error, when properly defined, of the sample coefficient of variation under a general distribution. We employ a Nagar-type expansion and use moments of quadratic forms to derive the results. We find that the approximate bias depends on not only the skewness but also the kurtosis of the distribution, whereas the approximate mean squared error depends on the cumulants up to order 6.

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