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On some inferential aspects of length biased log-logistic model

Author

Listed:
  • Ranjita Pandey

    (University of Delhi)

  • Pulkit Srivastava

    (University of Delhi)

  • Neera Kumari

    (BFIT)

Abstract

In this paper, we study a weighted distribution which is known to provide adjustment to the base distribution by ascertaining the probability of the actual occurrence of events vis-a-vis records and observations. Log-logistic distribution is a widely used time to event model with heavy tails. We introduce a two parameter length biased log-logistic distribution which is a special case of weighted distribution. Comprehensive description of its various mathematical properties is given. Moment generating function, order statistics and entropy aspects are examined. Stochastic orderings and likelihood ratio are also discussed. The proposed distribution is shown to have a promising potential as a better reliability model. Its advantage over five other popular time to event models is demonstrated through empirical fitting of a classical data set.

Suggested Citation

  • Ranjita Pandey & Pulkit Srivastava & Neera Kumari, 2021. "On some inferential aspects of length biased log-logistic model," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(1), pages 154-163, February.
  • Handle: RePEc:spr:ijsaem:v:12:y:2021:i:1:d:10.1007_s13198-020-01027-1
    DOI: 10.1007/s13198-020-01027-1
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    References listed on IDEAS

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    1. Steve Bennett, 1983. "Log‐Logistic Regression Models for Survival Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 32(2), pages 165-171, June.
    2. Joseph Reath & Jianping Dong & Min Wang, 2018. "Improved parameter estimation of the log-logistic distribution with applications," Computational Statistics, Springer, vol. 33(1), pages 339-356, March.
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