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Varentropy of Past Lifetimes

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Abstract

In a variety of applicative fields the level of information in random quantities is commonly measured by means of the Shannon Entropy. In particular, in reliability theory and survival analysis, time-dependent generalizations of this measure of uncertainty have been considered to dynamically describe changes in the degree of information over time. The Residual Entropy and the Residual Varentropy, for example, have been considered in the specialized literature to measure the information and its variability in residual lifetimes. In a similar way, one can consider dynamic measures of information for past lifetimes, i.e., for random lifetimes of items when one assumes that their failures occur before a fixed inspection time. This paper provides a study of the Past Varentropy, defined as the dynamic measure of variability of information for past lifetimes. From this study emerges the interest on a particular family of lifetimes distributions, whose members satisfy the property to be the only ones having constant Past Varentropy.

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REFERENCES

  1. P. K. Andersen, O. Borgan, R. D. Gill, and N. Keiding, Statistical Models Based on Counting Processes (Springer Verlag, New York, 1993).

    Book  MATH  Google Scholar 

  2. E. Arikan, ‘‘Varentropy decreases under polar transform,’’ IEEE Transactions on Information Theory 62, 3390–3400 (2016).

    Article  MathSciNet  MATH  Google Scholar 

  3. M. Asadi and A. Berred, ‘‘Properties and estimation of the mean past lifetime,’’ Statistics 46, 405–417 (2012).

    Article  MathSciNet  MATH  Google Scholar 

  4. R. E. Barlow and F. J. Proschan, Mathematical Theory of Reliability (Philadelphia: Society for Industrial and Applied Mathematics, 1996).

    Book  MATH  Google Scholar 

  5. H. W. Block and T. H. Savits, ‘‘The Reversed Hazard Rate Function,’’ Probability in the Engineering and Informational Sciences 12, 69–90 (1998).

    Article  MathSciNet  MATH  Google Scholar 

  6. S. Bobkov and M. Madiman, ‘‘Concentration of the information in data with log-concave distributions,’’ Annals of Probability 39, 1528–1543 (2011).

    Article  MathSciNet  MATH  Google Scholar 

  7. F. Buono, M. Longobardi, and M. Szymkowiak, ‘‘On generalized reversed aging intensity functions,’’ Ricerche mat 71, 85–108 (2022). https://doi.org/10.1007/s11587-021-00560-w

  8. T. Cacoullos and V. Papathanasiou, ‘‘On upper bounds for the variance of functions of random variables,’’ Statistics and Probability Letters 3, 175–184 (1985).

    Article  MathSciNet  MATH  Google Scholar 

  9. T. Cacoullos and V. Papathanasiou, ‘‘Characterizations of distributions by variance bounds,’’ Statistics and Probability Letters 7 (5), 351–356 (1989).

    Article  MathSciNet  MATH  Google Scholar 

  10. A. Di Crescenzo and M. Longobardi, ‘‘Entropy-based measure of uncertainty in past lifetime distributions,’’ Journal of Applied Probability 39, 434–440 (2002).

    Article  MathSciNet  MATH  Google Scholar 

  11. A. Di Crescenzo and L. Paolillo, ‘‘Analysis and applications of the residual varentropy of random lifetimes,’’ Probability in the Engineering and Informational Sciences 35 (3), 680–698 (2021).

    Article  MathSciNet  Google Scholar 

  12. N. Ebrahimi, ‘‘How to measure uncertainty in the residual life time distribution,’’ Sankhya: Series A 58, 48–56 (1996).

    MathSciNet  MATH  Google Scholar 

  13. M. S. Finkelstein, ‘‘On the Reversed Hazard Rate,’’ Reliability Engineering and System Safety 78, 71–75 (2002).

    Article  Google Scholar 

  14. M. Fradelizi, M. Madiman, and L. Wang, ‘‘Optimal Concentration of Information Content for Log-Concave Densities,’’ In: Houdré C., Mason D., Reynaud-Bouret P., Rosinski J. (eds) High Dimensional Probability VII. Progress in Probability 71, 45–60 (2016).

    Google Scholar 

  15. W. Gautschi and W. F. Gahill, Exponential Integral and Related Functions. Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, Ed. by M. Abramowitz and I. A. Stegun (New York: Dover, 1972).

    Google Scholar 

  16. F. Goodarzi,, M. Amini,, and G. R. Mohtashami Borzadaran, ‘‘On upper bounds for the variance of functions of the inactivity time,’’ Statistics and Probability Letters 117, 62–71 (2016).

    Article  MathSciNet  MATH  Google Scholar 

  17. R. M. Gray, Entropy and Information Theory (Springer, New York, 2011).

    Book  MATH  Google Scholar 

  18. R. C. Gupta and R. D. Gupta, ‘‘Proportional reversed hazard rate model and its applications,’’ Journal of Statistical Planning and Inference 137, 3525–3536 (2007).

    Article  MathSciNet  MATH  Google Scholar 

  19. A. M. Kandil, M. Kayid, and M. Mahdy, ‘‘Variance inactivity time function and its reliability properties,’’ The 45th Annual Conference on Statistics, Computer Science and Operations Research ISRR, Cairo-Egypt, 94–113 (2011).

  20. M. Kayid and S. Izadkhah, ‘‘Mean Inactivity Time Function, Associated Orderings, and Classes of Life Distributions,’’ IEEE Transactions on Reliability 63 (2), 593–602 (2014).

    Article  Google Scholar 

  21. I. Kontoyiannis and S. Verdú, ‘‘Optimal lossless data compression: non-asymptotics and asymptotics,’’ IEEE Transactions on Information Theory 60, 777–795 (2014).

    Article  MathSciNet  MATH  Google Scholar 

  22. H. Krishna and K. Kumar, ‘‘Reliability estimation in generalized inverted exponential distribution with progressively type II censored sample,’’ Journal of Statistical Computation and Simulation 83 (6), 1007–1019 (2013).

    Article  MathSciNet  MATH  Google Scholar 

  23. C. Kundu, A. Nanda, and S. Maiti, ‘‘Some distributional results through past entropy,’’ Journal of Statistical Planning and Inference 140, 1280–1291 (2010).

    Article  MathSciNet  MATH  Google Scholar 

  24. X. Li and Z. Li, ‘‘A Mixture Model of Proportional Reversed Hazard Rate,’’ Communications in Statistics — Theory and Methods 37 (18), 2953–2963 (2008). https://doi.org/10.1080/03610920802050935

    Article  MathSciNet  MATH  Google Scholar 

  25. C. T. Lin, B. S. Duran, and T. O. Lewis, ‘‘Inverted gamma as life distribution,’’ Microelectronics Reliability 29 (4), 619–626 (1989).

    Article  Google Scholar 

  26. M. Madiman and L. Wang, ‘‘An optimal varentropy bound for log-concave distributions,’’ International Conference on Signal Processing and Communications (SPCOM), Bangalore (2014). https://doi.org/10.1109/SPCOM.2014.6983953

  27. R. Morris, ‘‘The Dilogarithm Function of a Real Argument,’’ Mathematics of Computation 33, 778–787 (1979).

    Article  MathSciNet  MATH  Google Scholar 

  28. P. Muliere, G. Parmigiani, and N. G. Polson, ‘‘A Note on the Residual entropy Function,’’ Probability in the Engineering and Informational Sciences 7, 413–420 (1993).

    Article  Google Scholar 

  29. A. K. Nanda and S. Chowdhury, Shannon’s entropy and its generalizations towards statistics, reliability and information science during 1948–2018 (2019); arXiv:1901.09779v1.

  30. A. K. Nanda, H. Singh, N. Misra, and P. Paul, ‘‘Reliability properties of reversed residual lifetime,’’ Communications in Statistics—Theory and Methods 32, 2031–2042 (2003).

    Article  MathSciNet  MATH  Google Scholar 

  31. P. E. Oguntunde, A. O. Adejumo, and O. S. Balogun, ‘‘Statistical Properties of the Exponentiated Generalized Inverted Exponential Distribution,’’ Applied Mathematics 4 (2), 47–55 (2014).

    Google Scholar 

  32. L. Paolillo, A. Di Crescenzo, and A. Suárez-Llorens, Stochastic Comparisons, Differential Entropy, and Varentropy for Distributions Induced by Probability Density Functions. (2021), arXiv:2103.11038v1.

  33. M. Shaked and J. G. Shantikumar, Stochastic Orders (Springer, 2007).

    Book  MATH  Google Scholar 

  34. C. E. Shannon, ‘‘A mathematical theory of communication,’’ Bell System Technical Journal 27, 379–423 (1948).

    Article  MathSciNet  MATH  Google Scholar 

  35. T. Sun Han and K. Kobayashi, ‘‘Mathematics of Information and Coding,’’ American Mathematical Society (2002), ISBN:978-0-8218-0534-3.

    MATH  Google Scholar 

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ACKNOWLEDGMENTS

The authors would like to thank the reviewers for their constructive comments that greatly improved the paper.

During the preparation of the final revised version of this paper, the authors have been informed that some of their results can also be found in the recent paper

– Raqab M.Z., Bayoud H.A., and Qiu G. (2022). ‘‘Varentropy of inactivity time of a random variable and its related applications,’’ IMA Journal of Mathematical Control and Information 39 (1), 132–154, which has already appeared but was submitted after the submission to this journal of this work.

The authors are members of the research group GNAMPA of INdAM (Istituto Nazionale di Alta Matematica). Francesco Buono and Maria Longobardi are partially supported by MIUR — PRIN 2017, project ‘‘Stochastic Models for Complex Systems’’, no. 2017 JFFHSH. The present work was developed within the activities of the project 000009_ALTRI_CDA_75_2021_FRA_LINEA_B funded by ‘‘Programma per il finanziamento della ricerca di Ateneo — Linea B’’ of the University of Naples Federico II.

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Correspondence to Francesco Buono, Maria Longobardi or Franco Pellerey.

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Buono, F., Longobardi, M. & Pellerey, F. Varentropy of Past Lifetimes. Math. Meth. Stat. 31, 57–73 (2022). https://doi.org/10.3103/S106653072202003X

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  • DOI: https://doi.org/10.3103/S106653072202003X

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