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On a modified Burr XII distribution having flexible hazard rate shapes

  • Farrukh Jamal , Christophe Chesneau , M. Arslan Nasir , Abdus Saboor , Emrah Altun and M. Azam Khan
Published/Copyright: January 13, 2020
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Abstract

In this paper, we propose a new three-parameter modified Burr XII distribution based on the standard Burr XII distribution and the composition technique developed by [14]. Among others, we show that this technique has the ability to significantly increase the flexibility of the former Burr XII distribution, with respect to the density and hazard rate shapes. Also, complementary theoretical aspects are studied as shapes, asymptotes, quantiles, useful expansion, moments, skewness, kurtosis, incomplete moments, moments generating function, stochastic ordering, reliability parameter and order statistics. Then, a Monte Carlo simulation study is carried out to assess the performance of the maximum likelihood estimates of the modified Burr XII model parameters. Finally, three applications to real-life data sets are presented, with models comparisons. The results are favorable for the new modified Burr XII model.

  1. (Communicated by Gejza Wimmer)

Acknowledgement

The authors would like to thank the referees for their thorough comments which helped to improve the manuscript.

References

[1] Abdel-Hamid, A. H.: Constant-partially accelerated Life Tests for Burr XII distribution with progressive type II Censoring, Comput. Statist. Data Anal. 53 (2009), 2511–2523.10.1016/j.csda.2009.01.018Search in Google Scholar

[2] Afify, A. Z.—Altun, E.—Alizadeh, M.—Ozel, G.—Hamedani, G. G.: The odd exponentiated half-logistic-G family: properties, characterizations and applications, Chil. J. Stat. 8 (2017), 65–91.Search in Google Scholar

[3] Afify, A. Z.—Cordeiro, G. M.—Ortega, E. M. M.—Yousof, H. M.—Butt, N. S.: The four-parameter Burr XII distribution: properties, regression model and applications, Comm. Statist. Theory Methods 47 (2018), 2605–2624.10.1080/03610926.2016.1231821Search in Google Scholar

[4] Afify, A. Z.—Cordeiro, G. M.—Bourguignon, M.—Ortega, E. M. M.: Properties of the transmuted Burr XII distribution, regression and its applications, J. Data Sci. 16 (2018), 485–510.10.6339/JDS.201807_16(3).0003Search in Google Scholar

[5] AL-Hussaini, E. K.—Mousa, M. A.—Jaheen, Z. F.: Estimation under the Burr Type XII failure model: A comparative study, Test 1 (1992), 33–42.10.1007/BF02562661Search in Google Scholar

[6] Al-Saiari, A. Y.—Baharith, L. A.—Mousa, S. A.: Marshall-Olkin Extended Burr Type XII Distribution, Int. J. Stat. Probab. 3 (2014), 78–84.10.5539/ijsp.v3n1p78Search in Google Scholar

[7] Burr, I. W.: Cumulative frequency distributions, Ann. Math. Stat. 13 (1942), 215–232.10.1214/aoms/1177731607Search in Google Scholar

[8] Chen, G.—Balakrishnan, N.: A general purpose approximate goodness-of-fit test, Journal of Quality Technology 27 (1995), 154–161.10.1080/00224065.1995.11979578Search in Google Scholar

[9] Dasgupta, R.: On the distribution of Burr with applications, Sankhya 73 (2011), 1–19.10.1007/s13571-011-0015-ySearch in Google Scholar

[10] Decani, J. S.—Stine, R. A.: A note on deriving the information matrix for a logistic distribution, Amer. Statist. 40 (1986), 220–222.10.1080/00031305.1986.10475398Search in Google Scholar

[11] Drane, S. W.—Owen, D. B.—Seibetr Jr., G. B.: The Burr distribution and quantal responses, Statist. Papers 19 (1978), 204–210.10.1007/BF02932803Search in Google Scholar

[12] Gomes, E. E.—da-Silva, C. Q.—Cordeiro, G. M.: Two extended Burr models: theory and practice, Comm. Statist. Theory Methods 44 (2015), 1706–1734.10.1080/03610926.2012.762402Search in Google Scholar

[13] Kotz, S.—Lumelskii, Y.—Penskey, M.: The Stress-Strength Model and its Generalizations and Applications, World Scientific, Singapore, 2003.10.1142/9789812564511Search in Google Scholar

[14] Lai, C. D.—Xie, M.—Murthy, D. N. P.: A modified weibull distribution, IEEE Transactions on Reliability 52 (2003), 33–37.10.1109/TR.2002.805788Search in Google Scholar

[15] Lomax, K. S.: Business failures; Another example of the analysis of failure data, J. Amer. Statist. Assoc. 49 (1954), 847–852.10.1080/01621459.1954.10501239Search in Google Scholar

[16] Moore, D.—Papadopoulos, A. S.: The Burr Type XII distribution as a failure model under various Loss functions, Microelectronics Reliability 40 (2000), 2117–2122.10.1016/S0026-2714(00)00031-7Search in Google Scholar

[17] Muhammad, M.: A generalization of the Burr XII-Poisson distribution and its applications, J. Stat. Appl. Pro. 5 (2016), 29–41.10.18576/jsap/050103Search in Google Scholar

[18] Murthy, D. P.—Xie, M.—Jiang, R.: Weibull Models, John Wiley and Sons, 2004.Search in Google Scholar

[19] Nigm, A. M.—AL-Hussaini, E. K.—Jaheen, Z. F.: Bayesian one sample prediction of future observations under Pareto distribution, Statistics 37 (2003), 527–536.10.1080/02331880310001598837Search in Google Scholar

[20] Papadopoulos, A. S.: The Burr distribution as a life time model from a Bayesian approach, IEEE Transactions on Reliability 27 (1978), 369–371.10.1109/TR.1978.5220427Search in Google Scholar

[21] Paranaíba, P. F.—Ortega, E. M. M.—Cordeiro, G. M.—Pescim, R. R.: The beta Burr XII distribution with application to lifetime data, Comput. Statist. Data Anal. 55 (2011), 1118–1136.10.1016/j.csda.2010.09.009Search in Google Scholar

[22] Rodriguez, R. N.: A Guide to the Burr type XII distributions, Biometrika 64 (1977), 29–134.10.1093/biomet/64.1.129Search in Google Scholar

[23] Shaked, M.—Shanthikumar, J. G.: Stochastic Orders and their Applications, Academic Press, New York, 1994).Search in Google Scholar

[24] Shoukri, M. M.—Mian, I. U. H.—Tracy, D. S. Sampling properties of estimators of the log-logistic distribution with application to canadian precipitation data, Canad. J. Statist. 16 (1988), 223–236.10.2307/3314729Search in Google Scholar

[25] Tadikamalla, P. R.: A look at the Burr and related distributions, Int. Stat. Rev. 48 (1980), 37–344.10.2307/1402945Search in Google Scholar

[26] Yari, G.—Tondpour, Z.: The new Burr distribution and its application, Math. Sci. 11 (2017), 47–54.10.1007/s40096-016-0203-zSearch in Google Scholar

Received: 2019-04-09
Accepted: 2019-07-10
Published Online: 2020-01-13
Published in Print: 2020-02-25

© 2020 Mathematical Institute Slovak Academy of Sciences

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