A comparison of some confidence intervals for estimating the population coefficient of variation: a simulation study

This paper considers several confidence intervals for estim ating the population coefficient of variation based on parametric, nonparametric and modified m ethods. A simulation study has been conducted to compare the performance of the existing and new ly proposed interval estimators. Many intervals were modified in our study by estimating the va riance with the median instead of the mean and these modifications were also successful. Dat a were generated from normal, chi-square, and gamma distributions for CV = 0.1, 0.3, and 0. 5. We reported coverage probability and interval length for each estimator. The results were app lied to two public health data: child birth weight and cigarette smoking prevalence. Overall, go od intervals included an interval for chi-square distributions by McKay (1932), an interval esti mator for normal distributions by Miller (1991), and our proposed interval

[1]  T S Carey,et al.  Impact of socioeconomic status on hospital use in New York City. , 1993, Health affairs.

[2]  Mark G. Vangel,et al.  Confidence Intervals for a Normal Coefficient of Variation , 1996 .

[3]  H. Krishna,et al.  Asymptotic sampling distribution of inverse coefficient-of-variation and its applications , 1994 .

[4]  M. Smith,et al.  An unbiased signal-to-noise ratio measure for magnetic resonance images. , 1993, Medical physics.

[5]  Stanley Lemeshow,et al.  Applied Logistic Regression, Second Edition , 1989 .

[6]  Shipra Banik,et al.  Comparison of Some Parametric and Nonparametric Type One Sample Confidence Intervals for Estimating the Mean of a Positively Skewed Distribution , 2010, Commun. Stat. Simul. Comput..

[7]  David Hinkley,et al.  Bootstrap Methods: Another Look at the Jackknife , 2008 .

[8]  Shipra Banik,et al.  Estimating the Population Coefficient of Variation by Confidence Intervals , 2011, Commun. Stat. Simul. Comput..

[9]  B. Efron Bootstrap Methods: Another Look at the Jackknife , 1979 .

[10]  D. B. Owen,et al.  Confidence intervals for the coefficient of variation for the normal and log normal distributions , 1964 .

[11]  A. T. McKay,et al.  Distribution of the Coefficient of Variation and the Extended “T” Distribution , 1932 .

[12]  John A. H. Lee Health: United States , 1986 .

[13]  Ken Kelley,et al.  Sample size planning for the coefficient of variation from the accuracy in parameter estimation approach , 2007, Behavior research methods.

[14]  Ralph B. D'Agostino,et al.  Goodness-of-Fit-Techniques , 2020 .

[15]  P. Visintainer,et al.  Understanding and using confidence intervals in clinical research. , 1998, The Journal of maternal-fetal medicine.

[16]  G. E. Miller,et al.  Asymptotic test statistics for coefficients of variation , 1991 .

[17]  A. Lunde,et al.  Health in the United States , 1968, Nature.

[18]  D. Faber,et al.  Applicability of the coefficient of variation method for analyzing synaptic plasticity. , 1991, Biophysical journal.

[19]  Wararit Panichkitkosolkul,et al.  Improved Confidence Intervals for a Coefficient of Variation of a Normal Distribution , 2009 .

[20]  José Dias Curto,et al.  The coefficient of variation asymptotic distribution in the case of non-iid random variables , 2009 .

[21]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[22]  DeAnn Lazovich,et al.  Area-Level Variation in Adolescent Smoking , 2009, Preventing chronic disease.

[23]  B. M. Golam Kibria,et al.  On some confidence intervals for estimating the mean of a skewed population , 2007 .

[24]  T. Macdonald Preventing Chronic Diseases: A Vital Investment , 2006 .