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Pivotal Inference for the Generalized Logistic Distribution Based on Progressively Type-II Censored Samples

Yeon-Ju Seo, Suk-Bok Kang, Jung-In Seo

Abstract


This paper addresses estimation problems for unknown parameters of the generalized logistic distribution based on progressively Type-II censored samples. The maximum likelihood estimation method is the most popular method for estimating unknown parameters of probability distributions. However, it can entail a significant bias if the distribution is skewed or the sample is censored. To overcome this disadvantage, the paper proposes estimation
methods based on pivotal quantities and compares them with the maximum likelihood estimation method through Monte Carlo simulations for various progressively Type-II censoring schemes.

Keywords


Generalized Logistic Distribution, Pivotal Quantity, Progressively Type-II Censoring Schemes

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