Kybernetika 51 no. 6, 933-959, 2015

Compound geometric and Poisson models

Nooshin Hakamipour, Sadegh Rezaei and Saralees NadarajahDOI: 10.14736/kyb-2015-6-0933

Abstract:

Many lifetime distributions are motivated only by mathematical interest. Here, eight new families of distributions are introduced. These distributions are motivated as models for the stress of a system consisting of components working in parallel/series and each component has a fixed number of sub-components working in parallel/series. Mathematical properties and estimation procedures are derived for one of the families of distributions. A real data application shows superior performance of a three-parameter distribution (performance assessed with respect to Kolmogorov-Smirnov statistics, AIC values, BIC values, CAIC values, AICc values, HQC values, probability-probability plots, quantile-quantile plots and density plots) versus thirty one other distributions, each having at least three parameters.\\

Keywords:

exponential distribution, exponentiated exponential distribution, maximum likelihood estimation

Classification:

62E99

References:

  1. H. Akaike: A new look at the statistical model identification. IEEE Trans. Automat. Control 19 (1974), 716-723.   DOI:10.1109/tac.1974.1100705
  2. H. Bozdogan: Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions. Psychometrika 52 (1987), 345-370.   DOI:10.1007/bf02294361
  3. K. P. Burnham, D. and R. Anderson: Multimodel inference: Understanding AIC and BIC in model selection. Sociolog. Methods Res. 33 (2004), 261-304.   DOI:10.1177/0049124104268644
  4. T. S. Ferguson: A Course in Large Sample Theory. Chapman and Hall, London 1996.   DOI:10.1007/978-1-4899-4549-5
  5. R. C. Gupta, P. L. Gupta and R. D. Gupta: Modeling failure time data by Lehman alternatives. Commun. Statist. -- Theory and Methods 27 (1998), 887-904.   DOI:10.1080/03610929808832134
  6. R. D. Gupta and D. Kundu: Generalized exponential distributions. Australian and New Zealand J. Statist. 41 (1999), 173-188.   DOI:10.1111/1467-842x.00072
  7. E. J. Hannan and B. G. Quinn: The determination of the order of an autoregression. J. Royal Statist. Soc. B 41 (1979), 190-195.   CrossRef
  8. C. M. Hurvich and C.-L. Tsai: Regression and time series model selection in small samples. Biometrika 76 (1989), 297-307.   DOI:10.1093/biomet/76.2.297
  9. C. S. Kakde and D. T. Shirke: On exponentiated lognormal distribution. Int. J. Agricult. Statist. Sci. 2 (2006), 319-326.   CrossRef
  10. A. Kolmogorov: Sulla determinazione empirica di una legge di distribuzione. Giornale dell'Istituto Italiano degli Attuari 4 (1933), 83-91.   CrossRef
  11. K. Kolowrocki: Reliability of Large Systems. Elsevier, New York 2004.   CrossRef
  12. M. R. Leadbetter, G. Lindgren and H. Rootzén: Extremes and Related Properties of Random Sequences and Processes. Springer Verlag, New York 1987.   CrossRef
  13. L. E. Lehmann and G. Casella: Theory of Point Estimation. Second edition. Springer Verlag, New York 1998.   DOI:10.1007/b98854
  14. A. J. Lemonte and G. M. Cordeiro: The exponentiated generalized inverse Gaussian distribution. Statist. Probab. Lett. 81 (2011), 506-517.   DOI:10.1016/j.spl.2010.12.016
  15. A. W. Marshall and I. Olkin: A new method for adding a parameter to a family of distributions with application to the exponential and Weibull families. Biometrika 84 (1997), 641-652.   DOI:10.1093/biomet/84.3.641
  16. G. S. Mudholkar and D. K. Srivastava: Exponentiated Weibull family for analyzing bathtub failure-rate data. IEEE Trans. Reliability 42 (1993), 299-302.   CrossRef
  17. G. S. Mudholkar, D. K. Srivastava and M. Friemer: The exponential Weibull family: Analysis of the bus-motor-failure data. Technometrics 37 (1995), 436-445.   DOI:10.2307/1269735
  18. G. S. Mudholkar, D. K. Srivastava and G. D. Kollia: A generalization of the Weibull distribution with application to the analysis of survival data. J. Amer. Statist. Assoc. 91 (1996), 1575-1583.   DOI:10.2307/2291583
  19. S. Nadarajah: The exponentiated Gumbel distribution with climate application. Environmetrics 17 (2005), 13-23.   DOI:10.2307/2291583
  20. S. Nadarajah: The exponentiated exponential distribution: A survey. Adv. Statist. Anal. 95 (2011), 219-251.   DOI:10.1007/s10182-011-0154-5
  21. S. Nadarajah and A. K. Gupta: The exponentiated gamma distribution with application to drought data. Calcutta Statist. Assoc. Bull. 59 (2007), 29-54.   CrossRef
  22. S. Nadarajah and S. Kotz: The exponentiated type distributions. Acta Applic. Math. 92 (2006), 97-111.   DOI:10.1007/s10440-006-9055-0
  23. M. D. Nichols and W. J. Padgett: A bootstrap control chart for Weibull percentiles. Qual. Reliab. Engrg. Int. 22 (2006), 141-151.   DOI:10.1002/qre.691
  24. L. Qian: The Fisher information matrix for a three-parameter exponentiated Weibull distribution under type II censoring. Statist. Meth. 9 (2012), 320-329.   DOI:10.1016/j.stamet.2011.08.007
  25. R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria 2014.   CrossRef
  26. M. Ristic and S. Nadarajah: A new lifetime distribution. J. Statist. Comput. Simul. 84 (2014), 135-150.   DOI:10.1080/00949655.2012.697163
  27. G. E. Schwarz: Estimating the dimension of a model. Ann. Statist. 6 (1978), 461-464.   DOI:10.1214/aos/1176344136
  28. T. M. Shams: The Kumaraswamy-generalized exponentiated Pareto distribution. European J. Appl. Sci. 5 (2013), 92-99.   CrossRef
  29. N. Smirnov: Table for estimating the goodness of fit of empirical distributions. Ann. Math. Statist. 19 (1948), 279-281.   DOI:10.1214/aoms/1177730256