Unified Treatment for a Class of Extreme Value Distributions


Abstract


In this paper, we introduce new family depends on Fréchet distribution. A sub-model of the new family called the composed- Fréchet exponential (C-FE) distribution is presented to provide the flexibality of the family. The point and interval estimation based on maximum likelihood are proposed. We also obtain the Bayes estimates of the unknown parameters under the assumption of independent gamma priors. The Bayes estimates of the unknown parameters cannot be obtained in a closed form. So, Markov Chain Monte Carlo (MCMC) method has been used to compute the approximate Bayes estimates under the squared error loss function and also the highest posterior density (HPD) intervals have been constructed. Further, a simulation study has been conducted to compare the performances of the Bayes estimators with corresponding maximum likelihood estimators.


Keywords: Maximum likelihood estimation, Bayesian estimation, Monte Carlo Markov chain, Metropolis Hastings algorithm.

References


Alzaatreh, A., Lee, C. and Famoye, F. (2013). A new method for generating families of continuous distributions. Metron, 71, 63–79.

Barreto-Souza, W. M., Cordeiro, G. M. and Simas, A. B. (2011). Some results for beta Fréchet distribution. Commun. Statist. Theory-Meth., 40, 798-811.

Barlow, R. E., and Proschan, F. (1981). Statistical theory of reliability and life testing: probability models. FLORIDA STATE UNIV TALLAHASSEE

Chen, M. H. and Shao, Q. M. (1999). Monte Carlo estimation of Bayesian credible and HPD intervals. Journal of Computational and Graphical Statistics, 8, 69-92.

Cordeiro, G. M., Afify, A. Z., Ortega, E. M. M., Suzuki, A. K. and Mead, M. E.(2019). The odd Lomax generator of distributions: properties, estimation and applications. Journal of Computational and Applied Mathematics, 347, 222-237.

Cordeiro, G. M. and De Castro, M. (2011). A new family of generalized distributions. Journal of Statistical Computation and Simulation, 81, 883-898.

Dey, S., Dey. T. and Luckett, D. J. (2016). Statistical inference for the generalized inverted exponential distribution based on upper record values. Mathematics and Computers in Simulation, 120, 64-78.

Dey, S. and Dey, T. (2014). On progressively censored generalized inverted exponential distribution. Journal of Applied Statistics, 41, 2557– 2576.

Eugene, N., Lee, C. and Famoye, F. (2002).The beta-normal distribution and its applications. Communications in Statistics-Theory and Methods, 31, 497–512.

Fattah, A. A. and Ahmed, A. N. (2018). A unified approach for generalizing some families of probability distributions, with applications to reliability theory. Pakistan Journal of Statistics and Operation Research, 14, 253-273.

Fréchet, M. (1927). Sur la loi de probabilité de lécart maximum. Ann. de la Soc. polonaisede Math, 6, 93--116.

Gelfand, A. E. and Smith, A. F. M. (1990). Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85, 398-409.

Glaser, R. E.(1980). Bathtub and related failure rate characterizations, Journal of the American Statistical Association, 75 (371) (1980), 667-672. https://doi.org/10.1080/01621459.1980.10477530.

Kundu, D. and Pradhan, B. (2009). Bayesian inference and life testing plans for generalized exponential distribution. Science in China, 52, 1373–1388.

Mahmoud, M. R. and Mandouh, R. M. (2013). On the transmuted Fréchet distribution. Journal of Applied Sciences Research, 9, 5553-5561.

Mead, M. E. and Abd-Eltawab A.R. (2014). A note on Kumaraswamy Fréchet distribution. Aust. J. Basic & Appl. Sci., 8, 294-300.

Nadarajah, S. and Kotz, S. (2003). The exponentiated exponential distribution, Satistica, Available online at http://interstat.statjournals.net/YEAR/2003/abstracts/03 12001.php. 4

Nadarajah, S. and Kotz, S. (2008). Sociological models based on Fréchet random variables. Quality and Quantity, 42, 89-95.

Robert, C. and Casella, G. (2005). Monte Carlo Statistical Methods. Springer, New York, NY, USA.

Shaw, W. and Buckley, I. (2007). The alchemy of probability distributions: beyond Gram-Charlier expansions and a skew-kurtotic-normal distribution from a rank transmutation map. Research Report

Upadhyay, S. K. and Gupta,A. A. (2010). Bayes analysis of modified Weibull distribution via Markov chain Monte Carlo simulation, Journal of Statistical Computation and Simulation, 80, 241 – 254.

Zaharim, A., Najid, S.K., Razali, A.M. and Sopian, K. (2009). Analysing Malaysian wind speed data using statistical distribution. In Proceedings of the 4th IASME/WSEAS International conference on energy and environment, Cambridge, UK.


Full Text: pdf
کاغذ a4

Creative Commons License
This work is licensed under a Creative Commons Attribuzione - Non commerciale - Non opere derivate 3.0 Italia License.