Skip to main content
Log in

Testing exponentiality using mean residual quantile function

  • Regular Article
  • Published:
Statistical Papers Aims and scope Submit manuscript

Abstract

In the present paper, a non-parametric test is developed to test exponentiality using mean residual quantile function. Asymptotic distribution of the test statistic is derived. Simulation studies are carried out to assess the efficiency of the test. We also compare the power of the proposed test with the existing tests. We apply the proposed test to two real life data sets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Ahmad IA, Alwasel IA (1999) A goodness-of-fit test for exponentiality based on the memoryless property. J R Stat Soc 61(3):681–689

    Article  MathSciNet  MATH  Google Scholar 

  • Andersen PK, Gill RD, Keiding N (1993) Statistical models based on counting processes. Springer, New York

    Book  MATH  Google Scholar 

  • Angus JE (1982) Goodness-of-fit tests for exponentiality based on a loss-of-memory type functional equation. J Stat Plan Inference 6(3):241–251

    Article  MathSciNet  MATH  Google Scholar 

  • Bandyopadhyay D, Basu AP (1990) A class of tests for exponentiality against decreasing mean residual life alternatives. Commun Stat 19(3):905–920

    Article  MathSciNet  MATH  Google Scholar 

  • Baringhaus L, Henze N (2000) Tests of fit for exponentiality based on a characterization via the mean residual life function. Stat Pap 41(2):225–236

    Article  MathSciNet  MATH  Google Scholar 

  • Baringhaus L, Taherizadeh F (2013) A ks type test for exponentiality based on empirical hankel transforms. Commun Stat 42(20):3781–3792

    Article  MathSciNet  MATH  Google Scholar 

  • Bergman B, Klefsjö B (1989) A family of test statistics for detecting monotone mean residual life. J Stat Plan Inference 21(2):161–178

    Article  MATH  Google Scholar 

  • D’Agostino RB, Stephens MA (1986) Goodness-of-fit techniques. Marcel Dekker, New York

    MATH  Google Scholar 

  • Darling D (1957) The Kolmogorov–Smirnov, Cramer–von Mises tests. Ann Math Stat 28(4):823–838

    Article  MathSciNet  MATH  Google Scholar 

  • Durbin J (1975) Kolmogorov–Smirnov tests when parameters are estimated with applications to tests of exponentiality and tests on spacings. Biometrika 62(1):5–22

    Article  MathSciNet  MATH  Google Scholar 

  • Ebrahimi N, Habibullah M (1992) Testing exponentiality based on Kullback–Leibler information. J R Stat Soc 54:739–748

    MathSciNet  MATH  Google Scholar 

  • Gail M, Gastwirth J (1978) A scale-free goodness-of-fit test for the exponential distribution based on the gini statistic. J R Stat Soc 40:350–357

    MathSciNet  MATH  Google Scholar 

  • Gilchrist W (2000) Statistical modelling with quantile functions. CRC Press, Abingdon

    Book  Google Scholar 

  • Govindarajulu Z (1977) A class of distributions useful in life testing and reliability with applications to non-parametric testing. Theory Appl Reliab 1:109–130

    MathSciNet  Google Scholar 

  • Grubbs FE (1971) Approximate fiducial bounds on reliability for the two parameter negative exponential distribution. Technometrics 13(4):873–876

    Article  MATH  Google Scholar 

  • Guess F, Proschan F (1988) Mean residual life: theory and applications. In: Krishnaiah PR, Rao CR (eds) Quality control and reliability, handbook of statistics, vol 7. Elsevier, Amsterdam, pp 215–224

    Chapter  Google Scholar 

  • Hall WJ, Wellner JA (1981) Mean residual life. Stat Relat Top 169:184

    Google Scholar 

  • Hollander M, Proschan F (1972) Testing whether new is better than used. Ann Math Stat 43(4):1136–1146

    Article  MathSciNet  MATH  Google Scholar 

  • Jammalamadaka SR, Taufer E (2003) Testing exponentiality by comparing the empirical distribution function of the normalized spacings with that of the original data. Nonparametr Stat 15(6):719–729

    Article  MathSciNet  MATH  Google Scholar 

  • Jeong JH, Fine J (2009) A note on cause-specific residual life. Biometrika 96(1):237–242

    Article  MathSciNet  MATH  Google Scholar 

  • Kaplan EL, Meier P (1958) Nonparametric estimation from incomplete observations. J Am Stat Assoc 53(282):457–481

    Article  MathSciNet  MATH  Google Scholar 

  • Kotz S, Shanbhag DN (1980) Some new approaches to probability distributions. Adv Appl Probab 12(4):903–921. http://www.jstor.org/stable/1426748

  • Lai CD, Xie M (2006) Stochastic ageing and dependence for reliability. Springer Science Business Media, Inc., New York

    MATH  Google Scholar 

  • Marshall AW, Olkin I (2007) Life distributions: structure on nonparametric. Semiparametric and parametric familes. Springer, New York

    Google Scholar 

  • Midhu NN, Sankaran PG, Nair NU (2013) A class of distributions with the linear mean residual quantile function and it’s generalizations. Stat Methodol 15(0):1–24. http://www.sciencedirect.com/science/article/pii/S1572312713000294

  • Midhu NN, Sankaran PG, Nair NU (2014) A class of distributions with linear hazard quantile function. Commun Stat 43(17):3674–3689

    Article  MATH  Google Scholar 

  • Muth EJ (1977) Reliability models with positive memory derived from the mean residual life function. Theory Appl Reliab 2:401–434

    Google Scholar 

  • Nair NU, Sankaran PG (2008) Characterizations of multivariate life distributions. J Multivar Anal 99(9):2096–2107

    Article  MathSciNet  MATH  Google Scholar 

  • Nair NU, Sankaran PG (2009) Quantile-based reliability analysis. Commun Stat 38(2):222–232

    Article  MATH  Google Scholar 

  • Nair NU, Sankaran PG, Balakrishnan N (2013) Quantile-based reliability analysis. Birkhäuser, Basel

    Book  MATH  Google Scholar 

  • Padgett WJ (1986) A kernel-type estimator of a quantile function from right-censored data. J Am Stat Assoc 81(393):215–222

    Article  MathSciNet  MATH  Google Scholar 

  • Parzen E (1979) Nonparametric statistical data modeling. J Am Stat Assoc 74(365):105–121

    Article  MathSciNet  MATH  Google Scholar 

  • Patel JK (2004) Hazard rate and other classifications of distributions. Wiley, New York. doi:10.1002/0471667196.ess0935.pub2

    Book  Google Scholar 

  • Peng L, Fine J (2007) Nonparametric quantile inference with competing-risks data. Biometrika 94(3):735–744

    Article  MathSciNet  MATH  Google Scholar 

  • Sankaran PG, Midhu NN (2013) Nonparametric estimation of mean residual quantile function under right censoring (communicated)

  • Serfling RJ (1980) Approximation theorems of mathematical statistics. Wiley, New York

    Book  MATH  Google Scholar 

  • Shapiro SS (1995) Goodness of fit test. In: Balakrishnan N, Basu AP (eds) The exponential distribution: theory methods and applications. Gordon and Breach, Amsterdam

    Google Scholar 

  • Taufer E (2000) A new test for exponentiality against omnibus alternatives. Stoch Model Appl 3:23–36

    Google Scholar 

Download references

Acknowledgments

We thank reviewers and editor for their constructive comments.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to N. N. Midhu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sankaran, P.G., Midhu, N.N. Testing exponentiality using mean residual quantile function. Stat Papers 57, 235–247 (2016). https://doi.org/10.1007/s00362-014-0651-1

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00362-014-0651-1

Keywords

Navigation