Comparisons of Approximations to the Percentage Points of the Sample Coefficient of Variation

Technometrics ◽  
1970 ◽  
Vol 12 (1) ◽  
pp. 166 ◽  
Author(s):  
Boris Iglewicz ◽  
Raymond H. Myers
Keyword(s):  
Coefficient Of Variation ◽  
Percentage Points ◽  
Sample Coefficient Of Variation

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.

scholarly journals Domains of attraction of the real random vector (X,X2) and applications

2009 ◽  
Vol 86 (100) ◽  
pp. 41-53
Author(s):  
Edward Omey ◽  
Gulck van
Keyword(s):  
Asymptotic Behaviour ◽  
Random Vector ◽  
Coefficient Of Variation ◽  
Domain Of Attraction ◽  
Stable Law ◽  
Domains Of Attraction ◽  
Full Discussion ◽  
Definition Of ◽  
Sample Coefficient Of Variation ◽  
Sample Dispersion

Many statistics are based on functions of sample moments. Important examples are the sample variance s2(n), the sample coefficient of variation SV (n), the sample dispersion SD(n) and the non-central t-statistic t(n). The definition of these quantities makes clear that the vector defined by (?ni=1Xi, ?ni=1Xi2)plays an important role. In the paper we obtain conditions under which the vector (X,X2) belongs to a bivariate domain of attraction of a stable law. Applying simple transformations then leads to a full discussion of the asymptotic behaviour of SV(n) and t(n).

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.

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