The new generalized inverse Weibull distribution

Authors
Abstract
Failure rate is one of the important concepts in reliability theory. In this paper, we introduce a new distribution function containing four parameters based on inverse Weibull distribution. This new distribution has a more general form of failure rate function. It is able to model five ageing classes of life distributions with appropriate choice of parameter values so that it is displayed decreasing, increasing, bathtub shaped, unimodal and increasing-decreasing increasing failure rates and the new distribution has also a bimodal density function. The moments, the order statistics, reliability parameters are obtained. The method of maximum likelihood is used to estimate the model parameters. Also, the observed information matrix is obtained. Two applications are presented to illustrate the advantage of the proposed distribution.
Keywords

1. Aarset M.V., "How to identify bathtub hazard rate", IEEE Transactions on Reliability 36, (1987) 106-108. 2. Bebbington M., Lai C.D., Zitikis R., "A flexible Weibull extension", Reliability Engineering and System Safety 92 (2007) 719-726. 3. Barlow R.E., Campo R., "Total time on test processes and applications to failure data analysis", In: Reliability and Fault Tree Analysis. Society for Industrial and Applied Mathematics (1975) 451-481. 4. Carrasco J.M.F., Ortega E.M.M., Cordeiro G.M., "A generalized modified Weibull distribution for lifetime modeling", Comput Stat Data Anal, 53 (2008) 450-462. 5. Chen, Z., "A new two-parameter lifetime distribution with bathtub shape or increasing failure rate function", Statistics and Probability Letters 49 (200) 155-161 6. Felipe R.S., Edwin M.M.O., Cordeiro M., "The generalized inverse Weibull distribution", Stat Papers 52 (2011) 591-616. 7. Haupt E., Schabe H., "A new model for a lifetime distribution with bathtub shaped failure rate", Microelectronics and Reliability 32 (1992) 633-639. 8. Kundu D., Rakab M.Z., "Generalized Rayleigh distribution: different methods of estimation", Comput Stat Data Anal 49 (2005) 187-200. 9. Keller A.Z, Kamath A.R., "Reliability analysis of CNC machine tools", Reliab Eng 3 (1982) 449-473. 10. Lai C.D, Xie M., Murthy D.N.P., "A modified Weibull distribution", IEEE Transactions on Reliability 52 (2003) 33-37. 11. Lehmann E.L., Casella G. "Theory of point estimation", 2nd Edn. Chpman and Hall New York (1998). 12. Lee C, Famoye F, Olumolade O., "Beta-Weibull distribution: some properties and applications to censored data", J Mod Appl Stat Methods 6 (2007) 173-186. 13. Mudholkar G.S., Srivastava D.K., Friemer M., "The exponentiated Weibull family: A reanalysis of the bus-motor-failure data", Technometrics 37(1995) 436-445. 14. Pham H., Lai C.D., "On recent generalizations of the Weibulldistribution", IEEE Transactions on Reliability 56 (2004) 454-458. 15. Rajarshi S., Rajarshi M.B., "Bathtub distributions: A review. Communications in Statistics-Theory and Methods 17 (1988) 2521-2597. 16. Sarhan A.M., Zaindin M., "Modified Weibull distribution", Applied Sciences, Vol.11 (2009) 123-136. 17. Silva G.O., Edwin M.M.O., Gauss M.C., "The beta modified Weibull distribution", Lifetime Data Anal 16 (2010) 409-430. 18. Stollmack S., Harris C.M, "Failure-reate analysis applied to recidivism data", Operations Research 22(6) (1974) 1192-1205. 19. Xie M., Lai C.D., "Reliability analysis using an additive Weibull model with bathtub-shaped failure rate function", Reliability Engineering and System Safety 52 (1995) 87-93. 20. Xie M., Tang Y., Goh T.N., "A modified Weibull extension with bathtub failure rate function", Reliability Engineering and System Safety 76 (2002) 279-285.