Asymptotic sampling distribution of inverse coefficient-of-variation and its applications

Sharma, K.K.; Krishna, Hare

doi:10.1109/24.370217

Cited by 52 publications

(43 citation statements)

References 8 publications

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“…In a simulation study, Ng (2006) compared three confidence intervals for / proposed by McKay (1932) and elucidated by Pearson (1932), Sharma andKrishna (1994), andMiller (1991) for small values of , including = 0 05, 0.1, 0.2, 1/3, and 0.5. Ten thousand observations are made for each combination of values of n and .…”

Section: Comparisons With Simulation Results For / and /mentioning

confidence: 99%

“…In chemistry, the CV is used to compare the precision of two laboratories in measuring quantities of interest (see Reed et al, 2002). The coefficient of variation is used in numerous areas of science such as biology, economics, and psychology, and in engineering in queueing and reliability theory (see, for example, Sharma and Krishna, 1994). Nairy and Rao (2003) gave a summary of uses of the coefficient of variation in a number of areas.…”

Section: Introductionmentioning

confidence: 99%

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Influence Functions for the Coefficient of Variation, Its Inverse, and CV Comparisons

Groeneveld

2011

Communications in Statistics - Theory and Methods

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Expressions are found for the influence function of the coefficient of variation, CV, and its reciprocal, the signal to noise ratio. These functions are free of units, which permits the comparison of the values of the CVs of continuous positive distributions to a perturbation by a small amount of probability at x. For a CV ≤ 0 5, the influence function response will be negative, of modest size, for values of x near E(X). For such values of a CV and of x, the influence function for 1/CV will be positive and its values will be substantial. These results imply similar behavior by the sample coefficient of variation or its reciprocal, which is supported by simulation studies in the literature. Values of the CV ≥ 1 are associated with large negative responses of their influence functions. The distributions producing such responses often have densities that decrease from positive infinite to zero on the positive axis with a long tail to the right. An influence function for the difference of two coefficients of variation is also obtained.

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Section: Comparisons With Simulation Results For / and /mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Influence Functions for the Coefficient of Variation, Its Inverse, and CV Comparisons

Groeneveld

2011

Communications in Statistics - Theory and Methods

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“…Modification of Sharma and Krishna's (1994) Method using the bootstrap-t: We will reject the null hypothesis if…”

Section: Modified Methods Based On Bootstrap Percentile Pointsmentioning

confidence: 99%

“…Sharma and Krishna's (1994) Method: This is based on the CI for the inverted SNR developed by Sharma and Krishna (1994). We reject the null hypothesis if…”

Section: Parametric Estimatorsmentioning

confidence: 99%

“…Therefore, bootstrapping technique can be applied for estimating or hypothesis testing to all situations. For more information on the CI for the CV, we refer Koopmans et al (1964), Miller (1991), Sharma and Krishna (1994), McKay (1932), Vangel (1996), and Curto and Pinto (2009), among others. Very recently, George and Kibria (2012) considered several interval estimators for estimating the population SNR.…”

Section: Introductionmentioning

confidence: 99%

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Methods for Testing Population Signal-to-Noise Ratio

Kibria

George

2013

Communications in Statistics - Simulation and Computation

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This article considered several test statistics for testing the population signal-to-noise ratio based on parametric, nonparametric, and modified methods. To compare the performance of the proposed test statistics, a simulation study has been conducted under both symmetric and skewed distributions. The performance of the test statistics is compared based on the empirical size and power of the test. It is evident for large sample that some of our proposed test statistics are performing better in the sense of high power and have been recommended for the researchers.

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Coupling model uncertainty for coupled rainfall/runoff and surface water quality models in river problems

2012

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Coupled hydrology and water quality models are an important tool today, used in the understanding and management of surface water and watershed areas. Such problems are generally subject to substantial uncertainty in parameters, process understanding, and data. Component models, drawing on different data, concepts, and structures, are affected differently by each of these uncertain elements. This paper proposes a framework wherein the response of component models to their respective uncertain elements can be quantified and assessed, using a hydrological model and water quality model as two exemplars. The resulting assessments can be used to identify model coupling strategies that permit more appropriate use and calibration of individual models, and a better overall coupled model response. One key finding was that an approximate balance of water quality and hydrological model responses can be obtained using both the QUAL2E and Mike11 water quality models. The balance point, however, does not support a particularly narrow surface response (or stringent calibration criteria) with respect to the water quality calibration data, at least in the case examined here. Additionally, it is clear from the results presented that the structural source of uncertainty is at least as significant as parameter‐based uncertainties in areal models. Copyright © 2012 John Wiley & Sons, Ltd.

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Asymptotic sampling distribution of inverse coefficient-of-variation and its applications

Cited by 52 publications

References 8 publications

Influence Functions for the Coefficient of Variation, Its Inverse, and CV Comparisons

Influence Functions for the Coefficient of Variation, Its Inverse, and CV Comparisons

Methods for Testing Population Signal-to-Noise Ratio

Coupling model uncertainty for coupled rainfall/runoff and surface water quality models in river problems

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