International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

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 Volume 5, Issue 6 (June 2018), Pages: 64-69

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 Original Research Paper

 Title: Univariate and bivariate Burr x-type distributions

 Author(s): Mervat K. Abd Elaala *, Lamya A. Baharith

 Affiliation(s):

 Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia

 https://doi.org/10.21833/ijaas.2018.06.010

 Full Text - PDF          XML

 Abstract:

The Burr X distribution has been extensively studied by many researchers. It has many applications in medical, biological, agriculture and other fields. In this paper, a new family of Burr X-type distributions is introduced; the univariate Burr X-type distribution and the bivariate Burr X-type distribution. The bivariate Burr X-type distribution is constructed based on Gaussian copula with univariate Burr X-type distribution as marginals. This type distribution is more flexible and provides easier implementation and extension to bivariate form. A Gibbs sampler procedure is used to obtain Bayesian estimates of the unknown parameters. A simulation study is carried out to illustrate the efficiency of the proposed bivariate Burr X-type distribution. Finally, the proposed bivariate distribution is applied on real data to demonstrate its usefulness for real life applications. 

 © 2018 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Burr X distribution, M mixture representation, Copula, Bivariate Burr X type distribution, Gibbs sampler

 Article History: Received 24 January 2018, Received in revised form 30 March 2018, Accepted 9 April 2018

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2018.06.010

 Citation:

 Elaala MKA and Baharith LA (2018). Univariate and bivariate Burr x-type distributions. International Journal of Advanced and Applied Sciences, 5(6): 64-69

 Permanent Link:

 http://www.science-gate.com/IJAAS/2018/V5I6/Elaala.html

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 References (26) 

  1. Abd EBA, El-Adll ME, and ALOafi TA (2015). Estimation under Burr type X distribution based on doubly type II censored sample of dual generalized order statistics. Journal of the Egyptian Mathematical Society, 23(2): 391–396. https://doi.org/10.1016/j.joems.2014.03.011   [Google Scholar] 
  2. Abd Elaal MK, Mahmoud MR, EL-Gohary MM, and Baharith LA (2016). Univariate and bivariate Burr Type X distributions based on mixtures and copula. International Journal of Mathematics and StatisticsTM, 17(1): 113–127.   [Google Scholar]     
  3. Adham SA and Walker SG (2001). A multivariate Gompertz-type distribution. Journal of Applied Statistics, 28(8): 1051–1065. https://doi.org/10.1080/02664760120076706   [Google Scholar] 
  4. Adham SA, AL-Dayian GR, El Beltagy SH, and Abd Elaal MK (2009). Bivariate half- logistic-type distribution. Academy of Business Journal, AL-Azhar University, 2: 92–107.     
  5. Agarwal SK and Al-Saleh JA (2001). Generalized gamma type distribution and its hazard rate function. Communications in Statistics-Theory and Methods, 30(2): 309–318. https://doi.org/10.1081/STA-100002033   [Google Scholar] 
  6. AL Dayian GR, Adham SA, El Beltagy SH, and Elaal A (2008). Bivariate half-logistic distributions based on mixtures and copula. Academy of Business Journal, 2: 92–107.   [Google Scholar]     
  7. Al-Hussaini EK and Ateya SF (2005). Bayes estimations under a mixture of truncated type I generalized logistic components model. Journal of Statistical Theory and Applications, 4(2): 183–208.   [Google Scholar]     
  8. Ali Mousa MAM (2001). Inference and prediction for the Burr type X model based on records. Statistics: A Journal of Theoretical and Applied Statistics, 35(4): 415–425.   [Google Scholar]     
  9. Aludaat K, Alodat M, and Alodat T (2008). Parameter estimation of Burr type X distribution for grouped data. Applied Mathematical Sciences, 2(9): 415–423.   [Google Scholar]     
  10. Al-Urwi AS and Baharith LA (2017). A bivariate exponentiated Pareto distribution derived from Gaussian copula. International Journal of Advanced and Applied Sciences, 4(7): 66–73. https://doi.org/10.21833/ijaas.2017.07.010   [Google Scholar] 
  11. Arslan O (2005). A new class of multivariate distributions: Scale mixture of Kotz-type distributions. Statistics and Probability Letters, 75(1): 18–28. https://doi.org/10.1016/j.spl.2005.05.009   [Google Scholar]
  12. Burr IW (1942). Cumulative frequency functions. The Annals of Mathematical Statistics, 13(2): 215-232. https://doi.org/10.1214/aoms/1177731607   [Google Scholar] 
  13. Csörgő S and Welsh AH (1989). Testing for exponential and Marshall--Olkin distributions. Journal of Statistical Planning and Inference, 23(3): 287–300. https://doi.org/10.1016/0378-3758(89)90073-6   [Google Scholar]
  14. Genest C, Rémillard B, and Beaudoin D (2009). Goodness-of-fit tests for copulas: A review and a power study. Insurance: Mathematics and Economics, 44(2): 199–213.  https://doi.org/10.1016/j.insmatheco.2007.10.005   [Google Scholar] 
  15. Gilks WR and Wild P (1992). Adaptive rejection sampling for Gibbs sampling. Applied Statistics, 41(2): 337–348. https://doi.org/10.2307/2347565   [Google Scholar] 
  16. Gilks WR, Richardson S, and Spiegelhalter D (1995). Markov chain Monte Carlo in practice. CRC Press, Florida, USA.   [Google Scholar]  PMCid:PMC173322     
  17. Jaheen ZF and Al-Matrafi BN (2002). Bayesian prediction bounds from the scaled Burr type X model. Computers and Mathematics with Applications, 44(5): 587–594. https://doi.org/10.1016/S0898-1221(02)00173-6   [Google Scholar] 
  18. Johnson NL, Kotz S, and Balakrishnan N (2002). Continuous multivariate distributions (Vol. 1), models and applications (Vol. 59). John Wiley and Sons, New York, USA.   [Google Scholar]     
  19. Kjelsberg MO (1962). Estimation of the parameters of the logistic distribution under truncation and censoring. Ph.D. Dissertation, University of Minnesota, Minneapolis, USA.   [Google Scholar]    
  20. Kundu D and Gupta RD (2011). Absolute continuous bivariate generalized exponential distribution. AStA Advances in Statistical Analysis, 95(2): 169–185. https://doi.org/10.1007/s10182-010-0151-0   [Google Scholar] 
  21. Kundu D and Raqab MZ (2005). Generalized Rayleigh distribution: Different methods of estimations. Computational Statistics and Data Analysis, 49(1): 187–200. https://doi.org/10.1016/j.csda.2004.05.008     [Google Scholar] 
  22. Marshall AW and Olkin I (1997). A new method for adding a parameter to a family of distributions with application to the exponential and Weibull families. Biometrika, 84(3): 641–652. https://doi.org/10.1093/biomet/84.3.641   [Google Scholar] 
  23. Mudholkar GS and Srivastava DK (1993). Exponentiated Weibull family for analyzing bathtub failure-rate data. IEEE Transactions on Reliability, 42(2): 299–302. https://doi.org/10.1109/24.229504   [Google Scholar] 
  24. Raqab MZ (1998). Order statistics from the Burr type X model. Computers and Mathematics with Applications, 36(4): 111–120. https://doi.org/10.1016/S0898-1221(98)00143-6   [Google Scholar] 
  25. Robert CP (1995). Simulation of truncated normal variables. Statistics and Computing, 5(2): 121–125. https://doi.org/10.1007/BF00143942   [Google Scholar] 
  26. Walker SG and Stephens DA (1999). Miscellanea: A multivariate family of distributions on (0,∞) p. Biometrika, 86(3): 703–709. https://doi.org/10.1093/biomet/86.3.703   [Google Scholar]