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The alpha-mixture of survival functions

Published online by Cambridge University Press:  11 December 2019

Majid Asadi*
Affiliation:
University of Isfahan and IPM
Nader Ebrahimi*
Affiliation:
Northern Illinois University
Ehsan S. Soofi*
Affiliation:
University of Wisconsin-Milwaukee
*
*Postal address: Department of Statistics, University of Isfahan, Isfahan 81744, Iran. Email address: m.asadi@stat.ui.ac.ir
***Postal address: Department of Statistics, Northern Illinois University, DeKalb, IL 60155, USA. Email address: nebrahimi@niu.edu
****Postal address: Lubar School of Business, University of Wisconsin-Milwaukee, PO Box 742, Milwaukee, WI 53201, USA. Email address: esoofi@uwm.edu

Abstract

This paper presents a flexible family which we call the $\alpha$ -mixture of survival functions. This family includes the survival mixture, failure rate mixture, models that are stochastically closer to each of these conventional mixtures, and many other models. The $\alpha$ -mixture is endowed by the stochastic order and uniquely possesses a mathematical property known in economics as the constant elasticity of substitution, which provides an interpretation for $\alpha$ . We study failure rate properties of this family and establish closures under monotone failure rates of the mixture’s components. Examples include potential applications for comparing systems.

Type
Research Papers
Copyright
© Applied Probability Trust 2019 

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References

Aktekin, T. (2014). Call center service process analysis: Bayesian parametric and semi-parametric mixture modeling. Europ. J. Operat. Res. 234, 709719.CrossRefGoogle Scholar
Ardakani, O. M., Ebrahimi, N. and Soofi, E. S. (2018) Ranking forecasts by stochastic error distance, information, and reliability measures. Internat. Statist. Rev. 83, 442468.CrossRefGoogle Scholar
Arrow, K. J., Chenery, H. B., Minhas, B. S. and Solow, R. M. (1961). Capital–labor substitution and economic efficiency. Rev. Econom. Statist. 43, 225250.CrossRefGoogle Scholar
Asadi, M., Ebrahimi, N. and Soofi, E. S. (2018). Optimal hazard models based on partial information. Europ. J. Operat. Res. 270, 723733.CrossRefGoogle Scholar
Asadi, M., Ebrahimi, N., Kharazmi, O. and Soofi, E. S. (2019). Mixture models, Bayes Fisher information, and divergence measures. IEEE Trans. Inf. Theory 65, 23162321.CrossRefGoogle Scholar
Barlow, R. E. and Proschan, F. (1981). Statistical Theory of Reliability and Life Testing: Probability Models. To Begin With, Silver Spring, MD.Google Scholar
Barlow, R. E., Marshall, A. W. and Proschan, F. (1963). Properties of probability distributions with monotone hazard rate. Ann. Math. Statist. 34, 375389.CrossRefGoogle Scholar
Block, H. W. and Savits, T. H. (1997). Burn-in (with comments). Statist. Sci. 12, 119.Google Scholar
Block, H. W., Savits, T. H. and Wondmagegnehu, E. T. (2003). Mixtures of distributions with increasing linear failure rates. J. Appl. Prob. 40, 485504.CrossRefGoogle Scholar
Bullen, P. S. (2003). Handbook of Means and Their Inequalities, 2nd edn. Springer, Berlin.CrossRefGoogle Scholar
Diebold, F. X. and Shin, M. (2017). Assessing point forecast accuracy by stochastic error distance. Econometric Rev. 36, 588598.CrossRefGoogle Scholar
Esary, J. D., Marshall, A. W. and Proschan, F. (1970). Some reliability applications of the hazard transform. SIAM J. Appl. Math. 18, 849860.CrossRefGoogle Scholar
Finkelstein, M. S. (2009). Understanding the shape of the mixture failure rate (with engineering and demographic applications). Appl. Stoch. Models Business Industry 25, 643663.CrossRefGoogle Scholar
Hardy, G., Littlewood, J. E. and Pólya, G. (1934). Inequalities. Cambridge University Press.Google Scholar
Jiang, R. and Murthy, D. N. P. (1998). Mixture of Weibull distributions: Parametric characterization of failure rate function. Appl. Stoch. Models Data Anal. 14, 4765.3.0.CO;2-E>CrossRefGoogle Scholar
Li, Y., Gu, X. M. and Zhao, J. (2018). The weighted arithmetic mean–geometric mean inequality is equivalent to the Hölder inequality. Symmetry 10, 380384.CrossRefGoogle Scholar
Lynn, N. J. and Singpurwalla, N. D. (1997). Comment: Burn-in makes us feel good. Statist. Sci. 12, 1319.Google Scholar
Navarro, J. (2008). Likelihood ratio ordering of order statistics, mixtures and systems. J. Statist. Planning Infer. 138, 12421257.CrossRefGoogle Scholar
Navarro, J. and del Águila, Y. (2017). Stochastic comparisons of distorted distributions, coherent systems and mixtures with ordered components. Metrika 80, 627648.CrossRefGoogle Scholar
Navarro, J., del Águila, Y., Sordo, M. A. and Suárez-Llorens, A. (2016). Preservation of stochastic orders under the formation of generalized distorted distributions. Applications to coherent systems. Methodology Comput. Appl. Prob. 18, 529545.CrossRefGoogle Scholar
Shaked, M. and Shanthikumar, J. G. (2007). Stochastic Orders. Springer, New York.CrossRefGoogle Scholar
Uzawa, H. (1962). Production functions with constant elasticities of substitution. Rev. Econom. Stud. 29, 291299.CrossRefGoogle Scholar
van Erven, T. and Harremoës, P. (2014). Rényi divergence and Kullback–Leibler divergence. IEEE Trans. Inf. Theory 60, 37973820.CrossRefGoogle Scholar
Wondmagegnehu, E. T., Navarro, J. and Hernandez, P. J. (2005). Bathtub shaped failure rates from mixtures: A practical point of view. IEEE Trans. Reliab. 54, 270275.CrossRefGoogle Scholar