Skip to main content
SpringerLink
Log in

Bayesian inference for two log-logistic populations under joint progressive type II censoring schemes

  • Original Article
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

In this paper, we have discussed classical and Bayesian estimation of combined parameters of two different log-logistic models under a new type of censoring scheme known as joint progressive type II censoring scheme considering different scale parameters and common shape parameters. Maximum likelihood estimators are constructed with asymptotic confidence intervals. Then, Bayes estimators of parameters are proposed under different loss functions along with credible intervals and highest posterior density intervals. Markov Chain Monte Carlo approximation method has been used for simulation purpose. A real dataset has also been discussed for illustration.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  • Abo-Kasem, O. E. (2020). Statistical Inference for two Rayleigh Populations based on Joint Progressive Type II Censoring Schemes. Int J Recent Res Appl Stud 42(1)

  • Aldrich J (1997) RA Fisher and the making of maximum likelihood 1912–1922. Stat Sci 12(3):162–176

    Article  MathSciNet  MATH  Google Scholar 

  • Al-Shomrani AA, Shawky AI, Arif OH, Aslam M (2016) Log-logistic distribution for survival data analysis using MCMC. Springerplus 5(1):1–16

    Article  Google Scholar 

  • Ashkar F, Mahdi S (2006) Fitting the log-logistic distribution by generalized moments. J Hydrol 328(3–4):694–703

    Article  Google Scholar 

  • Balakrishnan N, Su F, Liu KY (2015) Exact likelihood inference for k exponential populations under joint progressive type-II censoring. Commun Stat-Simulat Comput 44(4):902–923

    Article  MathSciNet  MATH  Google Scholar 

  • Bayoud HA, Raqab M Z (2021) Classical and Bayesian inferences for two Topp-Leone models under joint progressive Type-II censoring. Commun Stat-Simulat Comput 1-19

  • Calabria R, Pulcini G (1996) Point estimation under asymmetric loss functions for left truncated exponential samples. Commun Stat-Theory Methods 25(3):585–600

    Article  MathSciNet  MATH  Google Scholar 

  • Chen Q, Gui W (2022) Statistical inference of the generalized inverted exponential distribution under joint progressively type-II censoring. Entropy 24(5):576

    Article  MathSciNet  Google Scholar 

  • Collett D (2015) Modelling survival data in medical research. CRC Press, London

    Book  Google Scholar 

  • Doostparast M, Ahmadi MV, Ahmadi J (2013) Bayes estimation based on joint progressive Type II censored data under LINEX loss function. Commun Stat-Simulat Comput 42(8):1865–1886

    MathSciNet  MATH  Google Scholar 

  • Fan J, Gui W (2022) Statistical inference of inverted exponentiated rayleigh distribution under joint progressively type-II censoring. Entropy 24(2):171

    Article  MathSciNet  Google Scholar 

  • Fisk PR (1961) The graduation of income distributions. Econometrica: J Econ Soc 29(2):171–185

    Article  MATH  Google Scholar 

  • Guure CB (2015) Inference on the loglogistic model with right censored data. Austin Biom and Biostat 2(1):1015

    Google Scholar 

  • Hastings WK (1970) Monte carlo sampling methods using markov chains and their applications. Biometrika 57(1):97–109

    Article  MathSciNet  MATH  Google Scholar 

  • Hoel DG (1972) A representation of mortality data by competing risks. Biometrics 28(2):475–488

    Article  Google Scholar 

  • Krishna H, Goel R (2020) Inferences for two Lindley populations based on joint progressive type-II censored data. Commun Stat-Simul Comput 1–18. https://doi.org/10.1080/03610918.2020.1751851

  • Lawless JF (2003) Copyright2003 John Wiley & Sons, Inc. Statistical Models and Methods for Lifetime Data, 3, 577

  • Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21(6):1087–1091. https://doi.org/10.1063/1.1699114

    Article  MATH  Google Scholar 

  • Mondal S, Kundu D (2019) Point and interval estimation of Weibull parameters based on joint progressively censored data. Sankhya b 81(1):1–25

    Article  MathSciNet  MATH  Google Scholar 

  • Mondal S, Kundu D (2020) On the joint Type-II progressive censoring scheme. Commun Stat-Theory Methods 49(4):958–976

    Article  MathSciNet  MATH  Google Scholar 

  • Parsi S, Ganjali M, Sanjari Farsipour N (2011) Conditional maximum likelihood and interval estimation for two Weibull populations under joint type-II progressive censoring. Commun Stat-Theory Methods 40:2117–2135

    Article  MathSciNet  MATH  Google Scholar 

  • Rasouli A, Balakrishnan N (2010) Exact likelihood inference for two exponential populations under joint progressive type-II censoring. Commun Stat-Theory Method 39(12):2172–2191

    Article  MathSciNet  MATH  Google Scholar 

  • Raqab MZ, Bdair OM, Kundu D (2022) Comparison of estimates and predictors using joint type-II progressive censored samples from two generalized Rayleigh distribution. Thailand Statistician, Thailand

    Google Scholar 

  • Rohatgi VK, Saleh AME (2015) An introduction to probability and statistics. John Wiley & Sons, USA

    Book  MATH  Google Scholar 

  • Sewailem MF, Baklizi A (2019) Inference for the log-logistic distribution based on an adaptive progressive type-II censoring scheme. Cogent Mathematics & Stat 6(1):1684228

    Article  MathSciNet  MATH  Google Scholar 

  • Shokr EM, EL-Sagheer, R. M., Khder, M., & El-Desouky, B. S. (2022) Inferences for two Weibull Frechet Populations under joint progressive type-II censoring with applications in engineering chemistry. Appl Math 16(1):73–92

  • Shoukri MM, Mian IUH, Tracy DS (1988) Sampling properties of estimators of the log logistic distribution with application to Canadian precipitation data. Canadian J Stat 16(3):223–236

    Article  MathSciNet  MATH  Google Scholar 

  • Varian HR (1975) A Bayesian approach to real state assessment. In: Stephen, E. F. & Zellner, A. (Eds.) Studies in Bayesian econometrics and statistics in honor of leonard. J Savage,195-208. Amsterdam: North-Holland

Download references

Acknowledgements

First author gratefully acknowledges IoE grant from University of Delhi.

Funding

First author gratefully acknowledges IoE grant from University of Delhi.

Author information

Authors and Affiliations

  1. Department of Statistics, University of Delhi, 110007, Delhi, India

    Ranjita Pandey & Pulkit Srivastava

Authors
  1. Ranjita Pandey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pulkit Srivastava
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pulkit Srivastava.

Ethics declarations

Conflict of interest

The authors have no competing interests to declare that are relevant to the content of this article.

human and animal resource

This research does not involve any human participants and/or animals.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pandey, R., Srivastava, P. Bayesian inference for two log-logistic populations under joint progressive type II censoring schemes. Int J Syst Assur Eng Manag 13, 2981–2991 (2022). https://doi.org/10.1007/s13198-022-01769-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-022-01769-0

Keywords

Search

Navigation