Estimation for the extreme value distribution under progressive Type-I interval censoring
Sol-Ji Nam, Suk-Bok Kang
Abstract
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Sol-Ji Nam, Suk-Bok Kang
Abstract
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In this paper, we propose some estimators for the extreme value distribution based on the interval method and mid-point approximation method from the progressive Type-I interval censored sample. Because log-likelihood function is a non-linear function, we use a Taylor series expansion to derive approximate likelihood equations. We compare the proposed estimators in terms of the mean squared error by using the Monte Carlo simulation.
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In this paper, we propose some estimators for the extreme value distribution based on the interval method and mid-point approximation method from the progressive Type-I interval censored sample. Because log-likelihood function is a non-linear function, we use a Taylor series expansion to derive approximate likelihood equations. We compare the proposed estimators in terms of the mean squared error by using the Monte Carlo simulation.
Key concepts: Censoring (clinical trials), Estimator, Mathematics, Taylor series, Monte Carlo method, Statistics, Interval estimation, Applied mathematics