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@misc{Canty2017,
author = {Canty, Angelo and Ripley, Brian},
pages = {1--3},
title = {{boot: Bootstrap R (S-Plus) Functions. R package version 1.3-20}},
year = {2017}
}
@inproceedings{Payton1996,
abstract = {The coefficient of variation (CV), defined as the ratio of the standard deviation to the mean, is often used in experimental situations. The exact distribution of the sample CV from a normally distributed population is complicated and obtaining a confidence interval for the population CV in this situation would require using the non-central t distribution and sequential techniques (Koopmans, et al., 1964). This paper explores the use of approximate distributions in determining confidence limits for the CV. The gamma distribution is used to model data appropriate for the calculation of the CV. A Monte Carlo simulation is performed to evaluate the effectiveness of four different intervals developed in this paper. A data set from a forestry experiment is analyzed using one of these techniques.},
author = {Payton, Mark E.},
booktitle = {Conference on Applied Statistics in Agriculture},
doi = {10.4148/2475-7772.1320},
file = {:home/maanib/Documents/rprojects/DescObs/tmp/CONFIDENCE INTERVALS FOR THE COEFFICIENT OF VARIATION.pdf:pdf},
pages = {82--90},
title = {{Confidence Intervals for the Coefficient of Variation}},
year = {1996}
}
@article{Vangel1996,
author = {Vangel, Mark G},
issn = {0003-1305},
journal = {The American Statistician},
number = {1},
pages = {21--26},
publisher = {Taylor {\&} Francis Group},
title = {{Confidence intervals for a normal coefficient of variation}},
volume = {50},
year = {1996}
}
@article{McKay1932,
author = {McKay, A T},
issn = {0952-8385},
journal = {Journal of the Royal Statistical Society},
number = {4},
pages = {695--698},
publisher = {JSTOR},
title = {{Distribution of the Coefficient of Variation and the Extended" t" Distribution}},
volume = {95},
year = {1932}
}
@article{Kelley2007,
abstract = {The accuracy in parameter estimation approach to sample size planning is developed for the coefficient of variation, where the goal of the method is to obtain an accurate parameter estimate by achieving a sufficiently narrow confidence interval. The first method allows researchers to plan sample size so that the expected width of the confidence interval for the population coefficient of variation is sufficiently narrow. A modification allows a desired degree of assurance to be incorporated into the method, so that the obtained confidence interval will be sufficiently narrow with some specified probability (e.g., 85{\%} assurance that the 95 confidence interval width will be no wider than to units). Tables of necessary sample size are provided for a variety of scenarios that may help researchers planning a study where the coefficient of variation is of interest plan an appropriate sample size in order to have a sufficiently narrow confidence interval, optionally with somespecified assurance of the confidence interval being sufficiently narrow. Freely available computer routines have been developed that allow researchers to easily implement all of the methods discussed in the article.},
author = {Kelley, Ken},
doi = {10.3758/BF03192966},
file = {:home/maanib/Documents/rprojects/DescObs/tmp/Kelley{\_}BRM{\_}AIPE{\_}CV{\_}2007.pdf:pdf},
issn = {1554351X},
journal = {Behavior Research Methods},
number = {4},
pages = {755--766},
pmid = {18183888},
title = {{Sample size planning for the coefficient of variation from the accuracy in parameter estimation approach}},
volume = {39},
year = {2007}
}
@article{Altunkaynak2018,
abstract = {{\textcopyright} 2018 Taylor  {\&}  Francis Group, LLC In non-normal populations, it is more convenient to use the coefficient of quartile variation rather than the coefficient of variation. This study compares the percentile and t-bootstrap confidence intervals with Bonett's confidence interval for the quartile variation. We show that empirical coverage of the bootstrap confidence intervals is closer to the nominal coverage (0.95) for small sample sizes (n = 5, 6, 7, 8, 9, 10 and 15) for most distributions studied. Bootstrap confidence intervals also have smaller average width. Thus, we propose using bootstrap confidence intervals for the coefficient of quartile variation when the sample size is small.},
author = {Altunkaynak, Bulent and Gamgam, Hamza},
doi = {10.1080/03610918.2018.1435800},
file = {:home/maanib/Documents/rprojects/DescObs/tmp/Communications in Statistics - Simulation and Computation Volume issue 2018 [doi 10.1080{\%}2F03610918.2018.1435800] Altunkaynak, Bulent$\backslash$; Gamgam, Hamza -- Bootstrap confidence intervals for the coefficie.pdf:pdf},
issn = {15324141},
journal = {Communications in Statistics: Simulation and Computation},
keywords = {Bootstrap methods,Coefficient of dispersion,Coefficient of quartile variation,Coefficient of variation,Confidence interval},
number = {0},
pages = {1--9},
publisher = {Taylor {\&} Francis},
title = {{Bootstrap confidence intervals for the coefficient of quartile variation}},
url = {https://doi.org/10.1080/03610918.2018.1435800},
volume = {0},
year = {2018}
}
@article{Albatineh2014,
abstract = {In this paper, an evaluation of the performance of several confidence interval estimators of the population coefficient of variation ($\tau$) using ranked set sampling compared to simple random sampling is performed. Two performance measures are used to assess the confidence intervals for $\tau$, namely: width and coverage probabilities. Simulated data were generated from normal, log-normal, skew normal, Gamma, and Weibull distributions with specified population parameters so that the same values of $\tau$ are obtained for each distribution, with sample sizes n=15, 20, 25, 50, 100. A real data example representing birth weight of 189 newborns is used for illustration and performance comparison. {\textcopyright} 2013 Taylor {\&} Francis.},
author = {Albatineh, Ahmed N. and Kibria, B. M.Golam and Wilcox, Meredith L. and Zogheib, Bashar},
doi = {10.1080/02664763.2013.847405},
file = {:home/maanib/Documents/rprojects/DescObs/tmp/ConfidenceintervalestimationfortheCVRanksetsamplingJAS.pdf:pdf},
issn = {02664763},
journal = {Journal of Applied Statistics},
keywords = {coefficient of variation,confidence interval,coverage probability,ranked set sample,simple random sample},
number = {4},
pages = {733--751},
title = {{Confidence interval estimation for the population coefficient of variation using ranked set sampling: A simulation study}},
volume = {41},
year = {2014}
}
@article{EdwardMiller1991,
author = {{Edward Miller}, G},
issn = {0361-0926},
journal = {Communications in Statistics-Theory and Methods},
number = {10},
pages = {3351--3363},
publisher = {Taylor {\&} Francis},
title = {{Asymptotic test statistics for coefficients of variation}},
volume = {20},
year = {1991}
}
@article{Panichkitkosolkul2013,
abstract = {This paper presents three confidence intervals for the coefficient of variation in a normal distribution with a known population mean. One of the proposed confidence intervals is based on the normal approximation. The other proposed confidence intervals are the shortest-length confidence interval and the equal-tailed confidence interval. A Monte Carlo simulation study was conducted to compare the performance of the proposed confidence intervals with the existing confidence intervals. Simulation results have shown that all three proposed confidence intervals perform well in terms of coverage probability and expected length.},
author = {Panichkitkosolkul, Wararit},
doi = {10.1155/2013/324940},
file = {:home/maanib/Documents/rprojects/DescObs/tmp/324940.pdf:pdf},
issn = {1687-952X},
journal = {Journal of Probability and Statistics},
pages = {1--11},
title = {{Confidence Intervals for the Coefficient of Variation in a Normal Distribution with a Known Population Mean}},
volume = {2013},
year = {2013}
}
@article{Mahmoudvand2009,
author = {Mahmoudvand, Rahim and Hassani, Hossein},
issn = {0266-4763},
journal = {Journal of Applied Statistics},
number = {4},
pages = {429--442},
publisher = {Taylor {\&} Francis},
title = {{Two new confidence intervals for the coefficient of variation in a normal distribution}},
volume = {36},
year = {2009}
}
@misc{Kelley2018,
author = {Kelley, Ken},
title = {{MBESS: The MBESS R Package. R package version 4.4. 3}},
url = {https://cran.r-project.org/package=MBESS},
year = {2018}
}
@article{Bonett2006,
abstract = {An approximate confidence interval is proposed for a robust measure of relative dispersion-the coefficient of quartile variation. The proposed method provides an alternative to interval estimates for other measures of relative dispersion. {\textcopyright} 2005 Elsevier B.V. All rights reserved.},
author = {Bonett, Douglas G.},
doi = {10.1016/j.csda.2005.05.007},
file = {:home/maanib/Documents/rprojects/DescObs/tmp/Computational Statistics {\&} Data Analysis Volume 50 issue 11 2006 [doi 10.1016{\%}2Fj.csda.2005.05.007] Douglas G. Bonett -- Confidence interval for a coefficient of quartile variation.pdf:pdf},
issn = {01679473},
journal = {Computational Statistics and Data Analysis},
keywords = {Coefficient of dispersion,Coefficient of variation,Interquartile range,Relative dispersion},
number = {11},
pages = {2953--2957},
title = {{Confidence interval for a coefficient of quartile variation}},
volume = {50},
year = {2006}
}
