Coefficient of variation and Power Pen's parade computation

Under the the assumption that income y is a power function of its rank among n individuals, we approximate the coefficient of variation and gini index as functions of the power degree of the Pen's parade. Reciprocally, for a given coefficient of variation or gini index, we propose the analytic expression of the degree of the power Pen's parade; we can then compute the Pen's parade.Gini index, Income inequality, Ranks, Har- monic Number, Pen's Parade.

The distribution of McKay's approximation for the coefficient of variation

McKay's approximation for the coefficient of variation is type II noncentral beta distributed and asymptotically normal with mean n - 1 and variance smaller than 2(n - 1)

Studies of the coefficient of variation of the magnitude of EEG signals

An analysis of the variation in magnitude of EEG signals in various frequency bands of anesthetized patients and normal sleeping volunteers was carried out. The coefficient of variation (CoV), i.e. the standard deviation/mean, within 10 second epochs was found to be quite constant throughout the whole of the EEG recordings and was typically about 0.46. This was found to be the case for both the patients and the volunteers. Histograms of the magnitudes indicated that the magnitudes are distributed as f(x)=βxe(-αx2) functions. However a CoV of 0.46 is consistent with f(x)=βxe(-αx3) functions. The non-stationary nature of the EEG is such that it is likely that while over short periods the EEG magnitudes are distributed as f(x)=βxe(-αx3) functions, variations of α over time mean that in the long term the EEG magnitudes are distributed as f(x)=βxe(-αx2) functions

Testing the Population Coefficient of Variation

The coefficient of variation (CV), which is used in many scientific areas, measures the variability of a population relative to its mean and standard deviation. Several methods exist for testing the population CV. This article compares a proposed bootstrap method to existing methods. A simulation study was conducted under both symmetric and skewed distributions to compare the performance of test statistics with respect to empirical size and power. Results indicate that some of the proposed methods are useful and can be recommended to practitioners

Change detection in SAR time-series based on the coefficient of variation

This paper discusses change detection in SAR time-series. Firstly, several statistical properties of the coefficient of variation highlight its pertinence for change detection. Then several criteria are proposed. The coefficient of variation is suggested to detect any kind of change. Then other criteria based on ratios of coefficients of variations are proposed to detect long events such as construction test sites, or point-event such as vehicles. These detection methods are evaluated first on theoretical statistical simulations to determine the scenarios where they can deliver the best results. Then detection performance is assessed on real data for different types of scenes and sensors (Sentinel-1, UAVSAR). In particular, a quantitative evaluation is performed with a comparison of our solutions with state-of-the-art methods

Variation in the “coefficient of variation”

The coefficient of variation (CV), also known as relative standard deviation, has been used to measure the constancy of the Weber fraction, a key signature of efficient neural coding in time perception. It has long been debated whether or not duration judgments follow Weber's law, with arguments based on examinations of the CV. However, what has been largely ignored in this debate is that the observed CVs may be modulated by temporal context and decision uncertainty, thus questioning conclusions based on this measure. Here, we used a temporal reproduction paradigm to examine the variation of the CV with two types of temporal context: full-range mixed vs. sub-range blocked intervals, separately for intervals presented in the visual and auditory modalities. We found a strong contextual modulation of both interval-duration reproductions and the observed CVs. We then applied a two-stage Bayesian model to predict those variations. Without assuming a violation of the constancy of the Weber fraction, our model successfully predicted the central-tendency effect and the variation in the CV. Our findings and modeling results indicate that both the accuracy and precision of our timing behavior are highly dependent on the temporal context and decision uncertainty. And, critically, they advise caution with using variations of the CV to reject the constancy of the Weber fraction of duration estimation