Testing the Population Coefficient of Variation (original) (raw)
Related papers
This paper considers several confidence intervals for estimating the population coefficient of variation based on parametric, nonparametric and modified methods. A simulation study has been conducted to compare the performance of the existing and newly proposed interval estimators. Many intervals were modified in our study by estimating the variance with the median instead of the mean and these modifications were also successful. Data 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 applied to two public health data: child birth weight and cigarette smoking prevalence. Overall, good intervals included an interval for chi-square distributions by , an interval estimator for normal distributions by , and our proposed interval. MSC: Primary 62F10; Secondary 62F35
In this paper, an evaluation of the performance of several confidence interval estimators of the population coefficient of variation (τ) using ranked set sampling compared to simple random sampling is performed. Two performance measures are used to assess the confidence intervals for τ, 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 τ 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.
American Journal of Applied Sciences, 2017
In this study a novel statistical test is derived for the Coefficient of Variation (CV) under normal distributions. This is a newly derived test with value to engineering sciences in aspects of production of accurate items. The CV can measure the precision of a measuring instrument, among other applications. In order to determine instrument reliability, start by generating measures using the instrument. The CV is then calculated to determine if the measures generated by the instrument are concentrated around a central point. In use of normal distribution presumption, or approximation, applicable properties of the normal distributions lead to involvement of the chi-square and t-distributions. A CV test is then constructed, and two illustrative examples conclude the discussion.
A parametric bootstrap approach for the equality of coefficients of variation
Computational Statistics, 2013
In this article, a parametric bootstrap approach for testing the equality of coefficient of variation of k normal populations is proposed. Simulations show that the actual size of our proposed test is close to the nominal level, irrespective of the number of populations and sample sizes, and that this new approach is better than the other existing ones. Also, the power of our approach is satisfactory. An example is proposed for illustrating our new approach.
An Evaluation of the Bootstrap Hypothesis Using Computer Simulation
1984
A normally distributed data set of 1,000 values ranging from 50 to 150, with a mean of 50 and a standard deviation of 20-was created in order to evaluate the bootstrap method of repeated random sampling. Nine bootstrap samples of N=10 and nine more bootstrap samples of N=25 were randomly selected. One thousand random samples were selected from each of the 18 bootstrap samples, and its mean and standard deviation were calculated. The cumulative means and standard deviations diverged from the parameter values as often, and to the same extent, as they converged toward them. It was also concluded that the bootstrap procedure was biased because it did not continue to approach the universe parameter as the number of iterations increased. The limit of convergence was not the universe parameter. Hence, the bootstrap hypothesis regarding point estimates of means and standard deviations was not supported. (Author/(DC)
An Improved Estimator of Population Variance using known Coefficient of Variation
In the present article, an improved estimator (s 2 y k) over usual unbiased estimator of population variance (s 2 y) is proposed by using known coefficient of variation (C y) of the study variable y. Asymptotic expression for its bias and mean square error (MSE) have been obtained. For more practical utility the study of proposed estimator under estimated optimum value of k has also been carried out. A comparative study has been made between the proposed estimator and the conventional estimator. Numerical illustration is also given in support of the present study.
BOOTSIE – ESTIMATION OF COEFFICIENT OF VARIATION OF AFLP DATA BY BOOTSTRAP ANALYSIS
Bootsie is an English-native replacement for ASG Coelho's " DBOOT " utility for estimating coefficient of variation of a population of AFLP marker data using bootstrapping. Bootsie improves on DBOOT by supporting batch processing, time-to-completion estimation, built-in graphs, and a suite of export tools for creating data files for other population genetics software. Bootsie is released as open-source software under the Apache 2.0 license and is available for any Java SE 6 platform at
2016
www.idescat.cat/sort/ A comparison of some confidence intervals for estimating the population coefficient of variation: a simulation study