BibTex RIS Cite

Statistical inference for the burr type III distribution under type II censored data

Year 2017, Volume: 66 Issue: 2, 297 - 310, 01.08.2017
https://doi.org/10.1501/Commua1_0000000820

Abstract

In this study, estimation and prediction problems for the Burr typeIII distribution under type II censored data are considered. Maximum likelihood and maximum product spacing estimation methods are used to estimatemodel parameters. EM algorithm is employed to obtain maximum likelihoodestimates. Unobserved future order statistics are predicted with best unbiasedprediction method. A simulation study is carried out to exhibit performanceof the estimation methods. Further, a numerical example is presented to illustrate the usefulness of the Burr III distribution

References

  • Burr, I. W. (1942). Cumulative frequency functions. The Annals of mathematical statistics 13(2): 215-232.
  • Lindsay, S. R., et al. (1996). Modelling the diameter distribution of forest stands using the Burr distribution. Journal of Applied Statistics 23(6): 609-619.
  • Shao, Q. (2000). Estimation for hazardous concentrations based on NOEC toxicity data: an alternative approach. Environmetrics 11(5): 583-595.
  • Zimmer, W. J., et al. (1998). The Burr XII distribution in reliability analysis. Journal of Quality Technology 30(4): 386.
  • AbdelGhaly, A. A., et al. (1997). The use of Burr type XII distribution on software reliability growth modelling. Microelectronics and Reliability 37(2): 305-313.
  • Shankar, G. and Sahani, V. (1994). The Study of a Maintenance Float Model with Burr Failure Distribution. Microelectronics and Reliability 34(9): 1513-1517.
  • Liu, P. H. and Chen, F. L. (2006). Process capability analysis of non-normal process data using the Burr XII distribution. International Journal of Advanced Manufacturing Technology 27(9-10): 975-984.
  • Gove, J. H., et al. (2008). Rotated sigmoid structures in managed uneven-aged northern hardwood stands: a look at the Burr Type III distribution. Forestry 81(2): 161-176.
  • Kar, R., et al. (2010). A closed form Delay Evaluation Approach using Burr’s Distribution Function for High Speed On-Chip RC Interconnects. 2010 Ieee 2nd International Advance Computing Conference : 129-133.
  • Ganora, D. and Laio, F. (2015). Hydrological Applications of the Burr Distribution: Practical Method for Parameter Estimation. Journal of Hydrologic Engineering 20(11).
  • Rodriguez, R. N. (1977). A guide to the Burr type XII distributions. Biometrika 64(1): 129- 134.
  • Tadikamalla, P. R. (1980). A look at the Burr and related distributions. International Sta- tistical Review/Revue Internationale de Statistique : 337-344.
  • Zoraghi, N., et al. (2012). Estimating the four parameters of the Burr III distribution using a hybrid method of variable neighborhood search and iterated local search algorithms. Applied Mathematics and Computation 218(19): 9664-9675.
  • Wingo, D. R. (1993). Maximum likelihood estimation of Burr XII distribution parameters under type II censoring. Microelectronics Reliability 33(9): 1251-1257.
  • Wang, F.K. and Cheng, Y. (2010). EM algorithm for estimating the Burr XII parameters with multiple censored data. Quality and Reliability Engineering International 26(6): 615-630.
  • Panahi, H. and Sayyareh, A. (2014). Parameter estimation and prediction of order statistics for the Burr Type XII distribution with Type II censoring. Journal of Applied Statistics 41(1): 215-232.
  • Moradi, N., et al. (2014). Estimation of the parameters of a exponentiated Burr type III distribution under type II censoring. Journal of Statistical Sciences 8(1): 93-109.
  • Azizi, A. Z., et al. (2013). Inference about the Burr type III distribution under type-II hybrid censored data. Journal of Statistical Research of Iran 10(2): 209-233.
  • Singh, D. P., et al. (2016). Estimation and prediction for a Burr III distribution with progres- sive censoring. Communication in Statistics - Theory and Methods. Accepted manuscript. DOI:10.1080/03610926.2016.1213290.
  • Louis, T. A. (1982). Finding the observed information matrix when using the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological): 226-233.
  • Dempster, A. P., et al. (1977). Maximum likelihood from incomplete data via the EM algo- rithm. Journal of the Royal Statistical Society. Series B (Methodological): 1-38.
  • Ng, H., et al. (2002). Estimation of parameters from progressively censored data using EM algorithm. Computational Statistics & Data Analysis 39(4): 371-386.
  • Kundu, D. and Pradhan, B. (2009). Estimating the parameters of the generalized exponential distribution in presence of hybrid censoring. Communications in Statistics - Theory and Methods 38(12): 2030-2041.
  • Cheng, R. and Amin, N. (1983). Estimating parameters in continuous univariate distributions with a shifted origin. Journal of the Royal Statistical Society. Series B (Methodological): 394- 403.
  • Ranneby, B. (1984). The maximum spacing method. An estimation method related to the maximum likelihood method. Scandinavian Journal of Statistics : 93-112.
  • Ekström, M. (2008). Alternatives to maximum likelihood estimation based on spacings and the Kullback–Leibler divergence. Journal of Statistical Planning and Inference 138(6): 1778- 1791.
  • Ng, H., et al. (2012). Parameter estimation of three-parameter Weibull distribution based on progressively Type-II censored samples. Journal of Statistical Computation and Simulation 82(11): 1661-1678.
  • Mann, N. R. and Fertig, K. W. (1973). Tables for obtaining Weibull con…dence bounds and tolerance bounds based on best linear invariant estimates of parameters of the extreme-value distribution. Technometrics 15(1): 87-101.
  • Prakash, G. and Singh, D. (2009). A Bayesian shrinkage approach in Weibull type-II censored data using prior point information. REVSTAT–Statistical Journal 7(2): 171-187.
  • Crowder, M.J., et al. (1991). Statistical Analysis of Reliability Data. Great Britain: Chap- mann&Hall.
  • D’agostino, R.B. and Stephens, M.A. (1986). Goodness of Fit Techniques. New York: Marcel Dekker.
  • Current address : Ömer Altında¼g: Bilecik ¸Seyh Edebali University, Faculty of Arts and Science, Department of Statistics, Bilecik, Turkey.
  • E-mail address : omer.altindag@bilecik.edu.tr, omeraltindag87@gmail.com
  • Current address : Mehmet Niyazi Çankaya: U¸sak University, Faculty of Arts and Science, Department of Statistics, U¸sak, Turkey.
  • E-mail address : mehmet.cankaya@usak.edu.tr, mehmetncankaya@gmail.com
  • Current address : Abdullah Yalçınkaya: Ankara University, Faculty of Science, Department of Statistics, Ankara, Turkey.
  • E-mail address : ayalcinkaya@ankara.edu.tr
  • Current address : Halil Aydo¼gdu: Ankara University, Faculty of Science, Department of Sta- tistics, Ankara, Turkey.
  • E-mail address : aydogdu@ankara.edu.tr
Year 2017, Volume: 66 Issue: 2, 297 - 310, 01.08.2017
https://doi.org/10.1501/Commua1_0000000820

Abstract

References

  • Burr, I. W. (1942). Cumulative frequency functions. The Annals of mathematical statistics 13(2): 215-232.
  • Lindsay, S. R., et al. (1996). Modelling the diameter distribution of forest stands using the Burr distribution. Journal of Applied Statistics 23(6): 609-619.
  • Shao, Q. (2000). Estimation for hazardous concentrations based on NOEC toxicity data: an alternative approach. Environmetrics 11(5): 583-595.
  • Zimmer, W. J., et al. (1998). The Burr XII distribution in reliability analysis. Journal of Quality Technology 30(4): 386.
  • AbdelGhaly, A. A., et al. (1997). The use of Burr type XII distribution on software reliability growth modelling. Microelectronics and Reliability 37(2): 305-313.
  • Shankar, G. and Sahani, V. (1994). The Study of a Maintenance Float Model with Burr Failure Distribution. Microelectronics and Reliability 34(9): 1513-1517.
  • Liu, P. H. and Chen, F. L. (2006). Process capability analysis of non-normal process data using the Burr XII distribution. International Journal of Advanced Manufacturing Technology 27(9-10): 975-984.
  • Gove, J. H., et al. (2008). Rotated sigmoid structures in managed uneven-aged northern hardwood stands: a look at the Burr Type III distribution. Forestry 81(2): 161-176.
  • Kar, R., et al. (2010). A closed form Delay Evaluation Approach using Burr’s Distribution Function for High Speed On-Chip RC Interconnects. 2010 Ieee 2nd International Advance Computing Conference : 129-133.
  • Ganora, D. and Laio, F. (2015). Hydrological Applications of the Burr Distribution: Practical Method for Parameter Estimation. Journal of Hydrologic Engineering 20(11).
  • Rodriguez, R. N. (1977). A guide to the Burr type XII distributions. Biometrika 64(1): 129- 134.
  • Tadikamalla, P. R. (1980). A look at the Burr and related distributions. International Sta- tistical Review/Revue Internationale de Statistique : 337-344.
  • Zoraghi, N., et al. (2012). Estimating the four parameters of the Burr III distribution using a hybrid method of variable neighborhood search and iterated local search algorithms. Applied Mathematics and Computation 218(19): 9664-9675.
  • Wingo, D. R. (1993). Maximum likelihood estimation of Burr XII distribution parameters under type II censoring. Microelectronics Reliability 33(9): 1251-1257.
  • Wang, F.K. and Cheng, Y. (2010). EM algorithm for estimating the Burr XII parameters with multiple censored data. Quality and Reliability Engineering International 26(6): 615-630.
  • Panahi, H. and Sayyareh, A. (2014). Parameter estimation and prediction of order statistics for the Burr Type XII distribution with Type II censoring. Journal of Applied Statistics 41(1): 215-232.
  • Moradi, N., et al. (2014). Estimation of the parameters of a exponentiated Burr type III distribution under type II censoring. Journal of Statistical Sciences 8(1): 93-109.
  • Azizi, A. Z., et al. (2013). Inference about the Burr type III distribution under type-II hybrid censored data. Journal of Statistical Research of Iran 10(2): 209-233.
  • Singh, D. P., et al. (2016). Estimation and prediction for a Burr III distribution with progres- sive censoring. Communication in Statistics - Theory and Methods. Accepted manuscript. DOI:10.1080/03610926.2016.1213290.
  • Louis, T. A. (1982). Finding the observed information matrix when using the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological): 226-233.
  • Dempster, A. P., et al. (1977). Maximum likelihood from incomplete data via the EM algo- rithm. Journal of the Royal Statistical Society. Series B (Methodological): 1-38.
  • Ng, H., et al. (2002). Estimation of parameters from progressively censored data using EM algorithm. Computational Statistics & Data Analysis 39(4): 371-386.
  • Kundu, D. and Pradhan, B. (2009). Estimating the parameters of the generalized exponential distribution in presence of hybrid censoring. Communications in Statistics - Theory and Methods 38(12): 2030-2041.
  • Cheng, R. and Amin, N. (1983). Estimating parameters in continuous univariate distributions with a shifted origin. Journal of the Royal Statistical Society. Series B (Methodological): 394- 403.
  • Ranneby, B. (1984). The maximum spacing method. An estimation method related to the maximum likelihood method. Scandinavian Journal of Statistics : 93-112.
  • Ekström, M. (2008). Alternatives to maximum likelihood estimation based on spacings and the Kullback–Leibler divergence. Journal of Statistical Planning and Inference 138(6): 1778- 1791.
  • Ng, H., et al. (2012). Parameter estimation of three-parameter Weibull distribution based on progressively Type-II censored samples. Journal of Statistical Computation and Simulation 82(11): 1661-1678.
  • Mann, N. R. and Fertig, K. W. (1973). Tables for obtaining Weibull con…dence bounds and tolerance bounds based on best linear invariant estimates of parameters of the extreme-value distribution. Technometrics 15(1): 87-101.
  • Prakash, G. and Singh, D. (2009). A Bayesian shrinkage approach in Weibull type-II censored data using prior point information. REVSTAT–Statistical Journal 7(2): 171-187.
  • Crowder, M.J., et al. (1991). Statistical Analysis of Reliability Data. Great Britain: Chap- mann&Hall.
  • D’agostino, R.B. and Stephens, M.A. (1986). Goodness of Fit Techniques. New York: Marcel Dekker.
  • Current address : Ömer Altında¼g: Bilecik ¸Seyh Edebali University, Faculty of Arts and Science, Department of Statistics, Bilecik, Turkey.
  • E-mail address : omer.altindag@bilecik.edu.tr, omeraltindag87@gmail.com
  • Current address : Mehmet Niyazi Çankaya: U¸sak University, Faculty of Arts and Science, Department of Statistics, U¸sak, Turkey.
  • E-mail address : mehmet.cankaya@usak.edu.tr, mehmetncankaya@gmail.com
  • Current address : Abdullah Yalçınkaya: Ankara University, Faculty of Science, Department of Statistics, Ankara, Turkey.
  • E-mail address : ayalcinkaya@ankara.edu.tr
  • Current address : Halil Aydo¼gdu: Ankara University, Faculty of Science, Department of Sta- tistics, Ankara, Turkey.
  • E-mail address : aydogdu@ankara.edu.tr
There are 39 citations in total.

Details

Primary Language English
Journal Section Research Articles
Authors

Ö. Altındağ This is me

N. Çankaya M. This is me

A. Yalçınkaya This is me

H. Aydoğdu This is me

Publication Date August 1, 2017
Published in Issue Year 2017 Volume: 66 Issue: 2

Cite

APA Altındağ, Ö., Çankaya M., N., Yalçınkaya, A., Aydoğdu, H. (2017). Statistical inference for the burr type III distribution under type II censored data. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, 66(2), 297-310. https://doi.org/10.1501/Commua1_0000000820
AMA Altındağ Ö, Çankaya M. N, Yalçınkaya A, Aydoğdu H. Statistical inference for the burr type III distribution under type II censored data. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. August 2017;66(2):297-310. doi:10.1501/Commua1_0000000820
Chicago Altındağ, Ö., N. Çankaya M., A. Yalçınkaya, and H. Aydoğdu. “Statistical Inference for the Burr Type III Distribution under Type II Censored Data”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 66, no. 2 (August 2017): 297-310. https://doi.org/10.1501/Commua1_0000000820.
EndNote Altındağ Ö, Çankaya M. N, Yalçınkaya A, Aydoğdu H (August 1, 2017) Statistical inference for the burr type III distribution under type II censored data. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 66 2 297–310.
IEEE Ö. Altındağ, N. Çankaya M., A. Yalçınkaya, and H. Aydoğdu, “Statistical inference for the burr type III distribution under type II censored data”, Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat., vol. 66, no. 2, pp. 297–310, 2017, doi: 10.1501/Commua1_0000000820.
ISNAD Altındağ, Ö. et al. “Statistical Inference for the Burr Type III Distribution under Type II Censored Data”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 66/2 (August 2017), 297-310. https://doi.org/10.1501/Commua1_0000000820.
JAMA Altındağ Ö, Çankaya M. N, Yalçınkaya A, Aydoğdu H. Statistical inference for the burr type III distribution under type II censored data. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2017;66:297–310.
MLA Altındağ, Ö. et al. “Statistical Inference for the Burr Type III Distribution under Type II Censored Data”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, vol. 66, no. 2, 2017, pp. 297-10, doi:10.1501/Commua1_0000000820.
Vancouver Altındağ Ö, Çankaya M. N, Yalçınkaya A, Aydoğdu H. Statistical inference for the burr type III distribution under type II censored data. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2017;66(2):297-310.

Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.