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New approach for analysis of progressive Type-II censored data from the Pareto distribution
Commun. Stat. Appl. Methods, CSAM 2018;25:569-575
Published online September 30, 2018
© 2018 Korean Statistical Society.

Jung-In Seoa, Suk-Bok Kang1,b, Ho-Yong Kimb

aDepartment of Statistics, Daejeon University, Korea;
bDepartment of Statistics, Yeungnam University, Gyeongsan, Korea
Correspondence to: Department of Statistics, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk 38541, Korea. E-mail: sbkang@ynu.ac.kr
Received July 19, 2018; Revised August 7, 2018; Accepted August 7, 2018.
Pareto distribution is important to analyze data in actuarial sciences, reliability, finance, and climatology. In general, unknown parameters of the Pareto distribution are estimated based on the maximum likelihood method that may yield inadequate inference results for small sample sizes and high percent censored data. In this paper, a new approach based on the regression framework is proposed to estimate unknown parameters of the Pareto distribution under the progressive Type-II censoring scheme. The proposed method provides a new regression type estimator that employs the spacings of exponential progressive Type-II censored samples. In addition, the provided estimator is a consistent estimator with superior performance compared to maximum likelihood estimators in terms of the mean squared error and bias. The validity of the proposed method is assessed through Monte Carlo simulations and real data analysis.
Keywords : Pareto distribution, progressive Type-II censored sample, weighted linear regression