research-article

Confidence Intervals for Coefficient of Variation of Inverse Gaussian Distribution

Authors:

Wasana Chankham,

Sa-Aat Niwitpong,

Suparat NiwitpongAuthors Info & Claims

ICVISP 2019: Proceedings of the 3rd International Conference on Vision, Image and Signal Processing

Article No.: 73, Pages 1 - 6

https://doi.org/10.1145/3387168.3387254

Published: 25 May 2020 Publication History

Abstract

The coefficient of variation is an useful indicator to measure and compare separated data in different units. This paper proposes the new confidence interval for the single coefficient of variation and the difference between coefficients of variation of Inverse Gaussian distribution using the generalized confidence interval (GCI) and the bootstrap percentile confidence interval. A Monte Carlo simulation is used to construct and compare the performance of these confidence intervals based on the coverage probability and average length. The results of the simulation study showed that the GCI is an appropriate method to construct the confidence interval for the single coefficient of variation and the difference between coefficients of variation. The proposed approaches are illustrated based on the real data.

References

[1]

AG Shaper, SJ Pocock, AN Phillips and M Walker (1986). Identifying men at high risk of heart attacks: strategy for use in general practice. BMJ, 293, 474--478.

[2]

A Wongkhao, S Niwitpong and S Niwitpong (2015). Confidence intervals for the ratio of two independent coefficients of variation of normal distribution. Far East Journal of Mathematical Sciences, 98(6), 741--757.

[3]

B Efron and RJ Tibshirani (1993). An Introduction to Bootstrap, Chapman & Hall/CRC, Monographs on Statistics and Applied Probability, USA.

[4]

DK Srivastava and GS Mudholkar (2003). Goodness-of-fit tests for univariate and multivariate normal models. In: Khattree, R., Rao, C.R. (Eds.), Handbook of Statistics, Elsevier Science, Amsterdam, 22, 869--906.

[5]

E Schrödinger (1915). Zür theorie der fall-und steigversuche an teilchen mit Brownscer bewegung. Physikaliche Zeitschrift, 16, 289--295.

[6]

HE Akyuz and H Gamgam (2017). Bootstrap and jackknife approximate confidence intervals and performance comparisons for population coefficient of variation. Communication in Statistics: Simulation and Computation.

[7]

HK Hsieh (1990). Inferences on the coefficient of variation of an inverse Gaussian distribution. Communications in Statistics, 19(5), 1589--1605.

[8]

K Krishnamoorthy and L Tian (2008). Inferences on the difference and ratio of the means of two inverse Gaussian distributions. J. Statist. Plann, Inference, 133(7), 2082--2089.

[9]

K Krishnamoorthy and Y Lu (2003). Inference on the common mean of several normal populations based on the generalized variable method. Biometrics, 59(2), 237--247.

[10]

L Tian (2005). Inferences on the common coefficient of variation. Statistics in Medicine, 24(14), 2213--2220.

[11]

L Tian and J Wu (2007). Inferences on the mean response in a log-regression model: The generalized variable approach. Stat. Med. 26, 5180--5188.

[12]

M Gulhar, BM Golam Kibria, AN Albatineh, and NU Ahmed (2012). A comparison of some confidence intervals for estimating the population coefficient of variation: A simulation study. Statistics and Operations Research Transactions, 36(1), 45--68.

[13]

N Buntao and S Niwitpong (2013). Confidence intervals for the ratio of coefficients of variation of delta-Lognormal Distribution. Applied Mathematical Science, 7(77), 3811--3818.

[14]

P DE, JB Ghosh and CE Wells (1996) Scheduling to minimize the coefficient of variation. Int. J. Production Economics, 44(3), 249--253.

[15]

P Sangnawakij and S Niwitpong (2017). Confidence intervals for coefficients of variation in two-parameter exponential distributions. Communications in Statistics- Simulation and Computation, 46(8), 6618--6630.

[16]

R Mahmoudvand And H Hassani. (2009). Two new confidence intervals for the coefficient of variation in a normal distribution. Journal of Applied Statistics, 36(4), 429--442.

[17]

RD Ye and SG Wang (2008). Generalized inferences on the common mean in MANOVA models. Comm. Statist. Theory Methods, 37(14), 2291--2303.

[18]

RD YE, TF Ma and SG Wang (2010). Inferences on the common mean of several inverse Gaussian populations. Comput Stat Data Anal, 54, 906--915.

Digital Library

[19]

RJ Pavur, RL Edgeman and RC Scott (1992). Quadratic statistics for the goodness-of --fit test of the inverse Gaussian distribution. IEEE Transactions on Reliability, 41(1), 118--123.

[20]

RS Chhikara and JL Folks (1977). The inverse Gaussian distribution as a lifetime model.Technometrics, 19, 461--468.

[21]

RS Chhikara and JL Folks (1989). The inverse Gaussian distribution. Marcel Dekker, New York.

Digital Library

[22]

S Banik and BMG Kibria (2011). Estimating the population coefficient of variation by confidence intervals. Communication in Statistics- Simulation and Computation, 40(8), 1236--1261.

[23]

S Weerahandi (1993). Generalized confidence intervals. J. Amer. Statist. Assoc, 88(423), 899--905.

[24]

SH Lin and JC Lee (2005). Generalized inferences on the common mean of several normal populations. J. Statist. Plann. Inference, 134(2), 568--582.

[25]

H Lin, JC Lee and RS Wang (2007). Generalized inferences on the common mean vector of several multivariate normal populations. J. Statist. Plann. Inference, 137(7), 2240--2249.

[26]

TM Young, DG Perhac, FM Guess, and RV León (2008). Bootstrap confidence intervals for percentiles of reliability data for wood-plastic composits. Forest Products Journal, 58(11), 106--114.

[27]

X Liu, N Li and Y Hu (2015). Combining inferences on the common mean of several inverse Gaussian distributions based on confidence distribution. Stat Probab Lett. 105, 136--142.

[28]

YL Tsim, SP Yip, KS Tsang, KF Li and HF Wong (1991). Haematology and Serology. In Annual Report, Hong Kong Medical Technology Association Quality Assurance Programme, 25--40.

Cited By

Chankham WNiwitpong SNiwitpong S(2022)Measurement of dispersion of PM 2.5 in Thailand using confidence intervals for the coefficient of variation of an inverse Gaussian distributionPeerJ10.7717/peerj.1298810(e12988)Online publication date: 17-Feb-2022
https://doi.org/10.7717/peerj.12988

Index Terms

Confidence Intervals for Coefficient of Variation of Inverse Gaussian Distribution
1. Mathematics of computing
  1. Probability and statistics
    1. Probabilistic inference problems
      1. Hypothesis testing and confidence interval computation

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cover image ACM Other conferences

ICVISP 2019: Proceedings of the 3rd International Conference on Vision, Image and Signal Processing

August 2019

584 pages

ISBN:9781450376259

DOI:10.1145/3387168

Copyright © 2019 ACM.

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Publication History

Published: 25 May 2020

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ICVISP 2019

ICVISP 2019: 3rd International Conference on Vision, Image and Signal Processing

August 26 - 28, 2019

BC, Vancouver, Canada

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ICVISP 2019 Paper Acceptance Rate 126 of 277 submissions, 45%;

Overall Acceptance Rate 186 of 424 submissions, 44%

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Cited By

Chankham WNiwitpong SNiwitpong S(2022)Measurement of dispersion of PM 2.5 in Thailand using confidence intervals for the coefficient of variation of an inverse Gaussian distributionPeerJ10.7717/peerj.1298810(e12988)Online publication date: 17-Feb-2022
https://doi.org/10.7717/peerj.12988

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