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Different Approaches to Estimation of the Gompertz Distribution under the Progressive Type-II Censoring Scheme

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  • Kyeongjun Lee
  • Jung-In Seo
  • Alessandro De Gregorio

Abstract

This paper provides an estimation method for an unknown parameter by extending weighted least-squared and pivot-based methods to the Gompertz distribution with the shape and scale parameters under the progressive Type-II censoring scheme, which induces a consistent estimator and an unbiased estimator of the scale parameter. In addition, a way to deal with a nuisance parameter is provided in the pivot-based approach. For evaluation and comparison, the Monte Carlo simulations are conducted, and real data are analyzed.

Suggested Citation

  • Kyeongjun Lee & Jung-In Seo & Alessandro De Gregorio, 2020. "Different Approaches to Estimation of the Gompertz Distribution under the Progressive Type-II Censoring Scheme," Journal of Probability and Statistics, Hindawi, vol. 2020, pages 1-7, September.
    • Handle: RePEc:hin:jnljps:3541946
    DOI: 10.1155/2020/3541946
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    Cited by:

    1. Opeyo Peter Otieno & Weihu Cheng, 2023. "The Concavity of Conditional Maximum Likelihood Estimation for Logit Panel Data Models with Imputed Covariates," Mathematics, MDPI, vol. 11(20), pages 1-14, October.

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