Research Article
BibTex RIS Cite
Year 2020, Issue: 33, 1 - 11, 15.01.2021
https://doi.org/10.26650/ekoist.2020.33.810417

Abstract

References

  • Akaike, H. (1970). Statistical Predictor Identification, Annals of the Institute of Statistical Mathematics, 22, 203-217.
  • Akgul F.G., Şenoğlu B. and Arslan T. (2016). An alternative distribution to Weibull for modeling the wind speed data: Inverse Weibull distribution. Energy Conversion and Management, 114, p:234-240.
  • Alzaatreh, A., Famoye, F., Lee, C. (2013a) Weibull-Pareto Distribution and Its Applications. Communications in Statistics - Theory and Methods. 42:9, 1673-1691.
  • Alzaatreh, A., Lee, C., Famoye, F. (2013b) A new method for generating families of continuous distributions. Metron: International Journal of Statistics, 71: 63-79.
  • Bjerkedal T (1960) Acquisition of resistance in guinea pigs infected with different doses of virulent tubercle bacilli. Am J Hyg 72: 130-148.
  • Efron B (1988) Logistic regression, survival analysis and the Kaplan-Meier curve. Journal of the American Statistical Association 83(402):414-425.
  • Famoye, F., Lee, C., Olumolade, O. (2005). The beta-weibull distribution. J. Statist. Theor. Appl. 4(2):121–136. He B., Cui W. and Du X. (2016). An additive modified Weibull distribution. Reliability Engineering and System Safety, 145, p:28-37.
  • Hurvich, C.M. ve Tsai, C. (1989). Regression and Time Series Model Selection in Small Samples, Biometrika, 76, 297-307.
  • Kalbfleisch, J. D. and Prentice, R. L. (1980). The Statistical Analysis of Failure Time Data. NY: Wiley & Sons. First Edition, Appendix I, pp. 230-32.
  • Mudholkar, G. S., Kollia, G. D. (1994). Generalized Weibull family: A structural analysis. Commun. Statist. Theor. Meth. 23(4):1149–1171.
  • Mudholkar, G. S., Srivastava, D. K., Freimer, M. (1995). The exponentiated Weibull family: a reanalysis of the bus-motor-failure data. Technometrics 37(4):436–445.
  • Nasiru, S. and Luguterah, A. (2015). The New Weibull-Pareto Distribution. Pak. J. Stat.Oper.Res., 11(1), 103-114.
  • Schwarz, G. (1978). Estimating the Dimensions of a Model, The Annals of Statistics, 6, 461-464.
  • Weibull, W. (1951). Statistical distribution function of wide applicability. Journal of Applied Mechanics, 18, 292-297.

On Weibull-Pareto Distribution in Censored and Uncensored Data Structures

Year 2020, Issue: 33, 1 - 11, 15.01.2021
https://doi.org/10.26650/ekoist.2020.33.810417

Abstract

The Weibull distribution gives a flexible measurement that details the probability distribution associated with the lifetime characteristics of a particular part or service component. It is commonly used to assess product reliability, analyze life data, and model failure times. It is a versatile distribution that can take on the characteristics of other types of distributions, based on the value of the shape parameter. Weibull-Pareto distribution has been introduced as a new type of application of the Weibull distribution. In this article, it is investigated point and interval estimation for Weibull-Pareto distribution with censored and uncensored data. With real-time data applications, it is showed that Weibull-Pareto results are clearly better against Exponential, Gamma, and Weibull distributions results.

References

  • Akaike, H. (1970). Statistical Predictor Identification, Annals of the Institute of Statistical Mathematics, 22, 203-217.
  • Akgul F.G., Şenoğlu B. and Arslan T. (2016). An alternative distribution to Weibull for modeling the wind speed data: Inverse Weibull distribution. Energy Conversion and Management, 114, p:234-240.
  • Alzaatreh, A., Famoye, F., Lee, C. (2013a) Weibull-Pareto Distribution and Its Applications. Communications in Statistics - Theory and Methods. 42:9, 1673-1691.
  • Alzaatreh, A., Lee, C., Famoye, F. (2013b) A new method for generating families of continuous distributions. Metron: International Journal of Statistics, 71: 63-79.
  • Bjerkedal T (1960) Acquisition of resistance in guinea pigs infected with different doses of virulent tubercle bacilli. Am J Hyg 72: 130-148.
  • Efron B (1988) Logistic regression, survival analysis and the Kaplan-Meier curve. Journal of the American Statistical Association 83(402):414-425.
  • Famoye, F., Lee, C., Olumolade, O. (2005). The beta-weibull distribution. J. Statist. Theor. Appl. 4(2):121–136. He B., Cui W. and Du X. (2016). An additive modified Weibull distribution. Reliability Engineering and System Safety, 145, p:28-37.
  • Hurvich, C.M. ve Tsai, C. (1989). Regression and Time Series Model Selection in Small Samples, Biometrika, 76, 297-307.
  • Kalbfleisch, J. D. and Prentice, R. L. (1980). The Statistical Analysis of Failure Time Data. NY: Wiley & Sons. First Edition, Appendix I, pp. 230-32.
  • Mudholkar, G. S., Kollia, G. D. (1994). Generalized Weibull family: A structural analysis. Commun. Statist. Theor. Meth. 23(4):1149–1171.
  • Mudholkar, G. S., Srivastava, D. K., Freimer, M. (1995). The exponentiated Weibull family: a reanalysis of the bus-motor-failure data. Technometrics 37(4):436–445.
  • Nasiru, S. and Luguterah, A. (2015). The New Weibull-Pareto Distribution. Pak. J. Stat.Oper.Res., 11(1), 103-114.
  • Schwarz, G. (1978). Estimating the Dimensions of a Model, The Annals of Statistics, 6, 461-464.
  • Weibull, W. (1951). Statistical distribution function of wide applicability. Journal of Applied Mechanics, 18, 292-297.
There are 14 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Kamil Alakus 0000-0002-5092-8486

Necati Erilli 0000-0001-6948-0880

Publication Date January 15, 2021
Submission Date October 14, 2020
Published in Issue Year 2020 Issue: 33

Cite

APA Alakus, K., & Erilli, N. (2021). On Weibull-Pareto Distribution in Censored and Uncensored Data Structures. EKOIST Journal of Econometrics and Statistics(33), 1-11. https://doi.org/10.26650/ekoist.2020.33.810417