Power Transformations: Application for Symmetrizing the Distribution of Sample Coefficient of Variation from Inverse Gaussian Populations

2016 ◽  
pp. 127-137 ◽  
Author(s):  
Y Chaubey ◽  
A Sarker ◽  
M Singh
Keyword(s):  
Coefficient Of Variation ◽  
Inverse Gaussian ◽  
Power Transformations ◽  
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.

Stieltjes classes for moment-indeterminate probability distributions

2004 ◽  
Vol 41 (A) ◽  
pp. 281-294 ◽  
Author(s):  
Jordan Stoyanov
Keyword(s):  
Probability Distributions ◽  
Classical Problem ◽  
Inverse Gaussian ◽  
Positive Order ◽  
Power Transformations ◽  
Log Normal ◽  
Open Question ◽  
Indeterminate Probability ◽  
Index Of Dissimilarity ◽  
Finite Moments

Let F be a probability distribution function with density f. We assume that (a) F has finite moments of any integer positive order and (b) the classical problem of moments for F has a nonunique solution (F is M-indeterminate). Our goal is to describe a , where h is a ‘small' perturbation function. Such a class S consists of different distributions Fε (fε is the density of Fε) all sharing the same moments as those of F, thus illustrating the nonuniqueness of F, and of any Fε, in terms of the moments. Power transformations of distributions such as the normal, log-normal and exponential are considered and for them Stieltjes classes written explicitly. We define a characteristic of S called an index of dissimilarity and calculate its value in some cases. A new Stieltjes class involving a power of the normal distribution is presented. An open question about the inverse Gaussian distribution is formulated. Related topics are briefly discussed.

Sign in / Sign up

Export Citation Format

Share Document