Analysis and Applications of Quantile Approach on Residual Extropy

Authors

  • Amir Hamzeh Khammar University of Sistan and Baluchestan
  • Vahideh Ahrari
  • Seyed Mahdi Amir Jahanshahi University of Sistan and Baluchestan, Statistics Department.

DOI:

https://doi.org/10.19139/soic-2310-5070-1226

Keywords:

Distorted distribution, Quantile function, Nonparametric estimator, Reliability measures, Residual extropy, Stochastic orders, Uncertainty measure.

Abstract

Extropy is a measure of the uncertainty of a random variable. Motivated with the wideapplicability of quantile functions in modeling and analyzing statistical data, in this paper, we studyquantile version of the extropy from residual lifetime variable, "residual quantile extropy" in short.Unlike the residual extropy function, the residual quantile extropy determines the quantile densityfunction uniquely through a simple relationship. Aging classes, stochastic orders and characterizationresults are derived, using proposed quantile measure of uncertainty. We also suggest some applicationsrelated to (n i + 1)-out-of-n systems and distorted random variables. Finally, a nonparametricestimator for residual quantile extropy is provided. In order to evaluate of proposed estimator, we usea simulation study.

Author Biography

  • Seyed Mahdi Amir Jahanshahi, University of Sistan and Baluchestan, Statistics Department.
    Statistics Department

References

Baratpour, S. and Khammar, A.H. (2017). A quantile-based generalized dynamic cumulative measure of entropy. Communications in Statistics-Theory and Methods, 47, 3104-3117.
[2] Barlow, R.E. and Proschan, F. (1981). Statistical Theory of Reliability and Life Testing: Probability Models. Silver-Spring, To Begin With.
[3] Denneberg, D. (1990). Premium calculation: why standard deviation should be replaced by absolute deviation. Astin Bulletin, 20, 181-190.
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Published

2023-08-03

Issue

Vol. 11 No. 4 (2023)

Section

Research Articles

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How to Cite

Analysis and Applications of Quantile Approach on Residual Extropy. (2023). Statistics, Optimization & Information Computing, 11(4), 876-891. https://doi.org/10.19139/soic-2310-5070-1226