Abstract
This paper provides a new technique for constructing unbiased statistical prediction limits on order statistics of future samples using the results of a previous sample from the same underlying inverse Gaussian distribution. Statistical prediction limits for the inverse Gaussian distribution are obtained from a classical frequentist viewpoint. The results have direct application in reliability theory, where the time until the first failure in a group of several items in service provides a measure of assurance regarding the operation of the items. The statistical prediction limits are required as specifications on future life for components, as warranty limits for the future performance of a specified number of systems with standby units, and in various other applications. Prediction limit is an important statistical tool in the area of quality control. The lower prediction limits are often used as warranty criteria by manufacturers. The technique used here does not require the construction of any tables. It requires a quantile of the beta distribution and is conceptually simple and easy to use. The discussion is restricted to one-sided tolerance limits. For illustration, a numerical example is given.
Similar content being viewed by others
REFERENCES
Patel, J.K., Tolerance limits: A review, Commun. Stat.: Theory Methodol., 1986, vol. 15, pp. 2719–2762.
Dunsmore, J.R., Some approximations for tolerance factors for the two parameter exponential distribution, Technometrics, 1978, vol. 20, pp. 317–318.
Guenther, W.C., Patil, S.A., and Uppuluri, V.R.R., One-sided β-content tolerance factors for the two parameter exponential distribution, Technometrics, 1976, vol. 18, pp. 333–340.
Engelhardt, M. and Bain, L.J., Tolerance limits and confidence limits on reliability for the two-parameter exponential distribution, Technometrics, 1978, vol. 20, pp. 37–39.
Guenther, W.C., Tolerance intervals for univariate distributions, Nav. Res. Logist. Q., 1972, vol. 19, pp. 309–333.
Hahn, G.J. and Meeker, W.Q., Statistical Intervals: A Guide for Practitioners, New York: John Wiley & Sons, 1991.
Nechval, K.N. and Nechval, N.A., Constructing lower simultaneous prediction limits on observations in future samples from the past data, Comput. Ind. Eng., 1999, vol. 37, pp. 133–136.
Nechval, N.A. and Vasermanis, E.K., Improved Decisions in Statistics, Riga: Izglitibas soli, 2004.
Nechval, N.A. and Nechval, K.N., Tolerance limits on order statistics in future samples coming from the two-parameter exponential distribution, Am. J. Theor. Appl. Stat., 2016, vol. 5, pp. 1–6.
Nechval, N.A., Nechval, K.N., Prisyazhnyuk, S.P., and Strelchonok, V.F., Tolerance limits on order statistics in future samples coming from the Pareto distribution, Autom. Control Comput. Sci., 2016, vol. 50, pp. 423–431.
Nechval, N.A., Nechval, K.N., and Strelchonok, V.F., A new approach to constructing tolerance limits on order statistics in future samples coming from a normal distribution, Adv. Image Video Process., 2016, vol. 4, pp. 47–61.
Nechval, N.A., Berzins, G., Balina, S., Steinbuka, I., and Nechval, K.N., Constructing unbiased prediction limits on future outcomes under parametric uncertainty of underlying models via pivotal quantity averaging approach, Autom. Control Comput. Sci., 2017, vol. 51, pp. 331–346.
Nechval, N.A., Nechval, K.N., and Berzins, G., A new technique for constructing exact tolerance limits on future outcomes under parametric uncertainty, in Advanced Mathematical Techniques in Engineering Sciences, Ram, M. and Davim, J.P., Eds., London: Taylor & Francis Group, 2018, pp. 203–226.
Nechval, N.A., Nechval, K.N., and Berzins, G., A new technique for intelligent constructing exact γ-content tolerance limits with expected (1 – α) confidence on future outcomes in the Weibull case using complete or type II censored data, Autom. Control Comput. Sci., 2018, vol. 52, pp. 476–488.
Bhattacharyya, G.K. and Fries, A., Fatigue failure models—Birnbaum-Saunders vs. inverse Gaussian, IEEE Trans. Reliab., 1980, vol. R-31, pp. 439−440.
Goh, G.J., Tang, L.C., and Lim, S.C., Reliability modelling of stochastic wear-out failure, Reliab. Eng. Syst. Saf., 1989, vol. 25, pp. 303–314.
Jain, R.K. and Jain, S., Inverse Gaussian distribution and its application to reliability, Microelectron. Reliab., 1996, vol. 36, pp. 1323–1335.
Chhikara, R.S. and Folks, J.L., The Inverse Gaussian Distribution: Theory, Methodology, and Applications, New York: Marcel Dekker, Inc., 1989.
Seshadri, V., The Inverse Gaussian Distribution: A Case Study in Exponential Families, New York: Oxford University Press, 1993.
Seshadri, V., The Inverse Gaussian Distribution: Statistical Theory and Applications, New York: Springer, 1999.
Mudholkar, G.S. and Tian, L., An entropy characterization of the inverse Gaussian distribution and related goodness-of-fit tests, J. Stat. Plann. Inference, 2002, vol. 102, pp. 211–221.
Shuster, J., On the inverse Gaussian distribution function, J. Am. Stat. Assoc., 1968, vol. 63, pp. 1514–1516.
Zigangirov, K.Sh., Expression for the Wald distribution in terms of normal distribution, Radio Eng. Electron. Phys., 1962, vol. 7, pp. 145–148.
Chhikara, R.S. and Folks, J.L., Estimation of the inverse Gaussian distribution function, J. Am. Stat. Assoc., 1974, vol. 69, pp. 250–254.
Chhikara, R.S. and Folks, J.L., The inverse Gaussian distribution as a lifetime model, Technometrics, 1977, vol. 19, pp. 461–468.
Lieblein, J. and Zelen, M., Statistical investigation of the fatigue life of deep-groove ball bearing, J. Res. Natl. Bur. Stand., 1956, vol. 47, pp. 273–316.
Mee, R.W. and Kushary, D., Prediction limits for the Weibull distribution utilizing simulation, Comput. Stat. Data Anal., 1994, vol. 17, pp. 327–336.
Engelhardt, M. and Bain, L.J., On prediction limits for samples from a Weibull or extreme-value distribution, Technometrics, 1982, vol. 24, pp. 147–150.
Lawless, J.F., On the estimation of safe life when the underlying life distribution is Weibull, Technometrics, 1973, vol. 15, pp. 857–865.
ACKNOWLEDGMENTS
The authors would like to thank the anonymous reviewers for their valuable comments that helped to improve the presentation of this paper.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
There is no conflict of interest.
About this article
Cite this article
Nechval, N.A., Berzins, G., Nechval, K.N. et al. A New Technique of Intelligent Constructing Unbiased Prediction Limits on Future Order Statistics Coming from an Inverse Gaussian Distribution under Parametric Uncertainty. Aut. Control Comp. Sci. 53, 223–235 (2019). https://doi.org/10.3103/S0146411619030088
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.3103/S0146411619030088