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What is Pivot

Ranked Set Sampling Models and Methods
It is function of samples and unknown parameters whose probability distribution does not depends on the population characteristics.
Published in Chapter:
A Review of Bootstrap Methods in Ranked Set Sampling
Arpita Chatterjee (Georgia Southern University, USA) and Santu Ghosh (Augusta University, USA)
Copyright: © 2022 |Pages: 19
DOI: 10.4018/978-1-7998-7556-7.ch008
Abstract
This chapter provides a brief review of the existing resampling methods for RSS and its implementation to construct a bootstrap confidence interval for the mean parameter. The authors present a brief comparison of these existing methods in terms of their flexibility and consistency. To construct the bootstrap confidence interval, three methods are adopted, namely, bootstrap percentile method, bias-corrected and accelerated method, and method based on monotone transformation along with normal approximation. Usually, for the second method, the accelerated constant is computed by employing the jackknife method. The authors discuss an analytical expression for the accelerated constant, which results in reducing the computational burden of this bias-corrected and accelerated bootstrap method. The usefulness of the proposed methods is further illustrated by analyzing real-life data on shrubs.
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