On Optimal Progressive Censoring Schemes for Normal Distribution

U. H. Salemi, S. Rezaei, Y. Si and S. Nadarajah ()
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U. H. Salemi: Amirkabir University of Technology
S. Rezaei: Amirkabir University of Technology
Y. Si: University of Manchester
S. Nadarajah: University of Manchester

Annals of Data Science, 2018, vol. 5, issue 4, No 8, 637-658

Abstract: Abstract Selection of optimal progressive censoring schemes for the normal distribution is discussed according to maximum likelihood estimation and best linear unbiased estimation. The selection is based on variances of the estimators of the two parameters of the normal distribution. The extreme left censoring scheme is shown to be an optimal progressive censoring scheme. The usual type-II right censoring case is shown to be the worst progressive censoring scheme for estimating the scale parameter. It can greatly increase the variance of estimators.

Keywords: Approximate maximum likelihood; Best linear unbiased estimator; Monte Carlo simulations (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s40745-018-0156-1

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