A Comparison between Normal and Non-Normal Data in Bootstrap

Asmala, A. (2012) A Comparison between Normal and Non-Normal Data in Bootstrap. Applied Mathematical Sciences, 6 (92). 4547 -4560. ISSN 1312-885X


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In the area of statistics, bootstrapping is a general modern approach to resampling methods. Bootstrapping is a way of estimating an estimator such as a variance when sampling from a certain distribution. The approximating distribution is based on the observed data. A set of observations is a population of independent and observed data identically distributed by resampling; the set is random with replacement equal in size to that of the observed data. The study starts with an introduction to bootstrap and its procedure and resampling. In this study, we look at the basic usage of bootstrap in statistics by employing R. The study discusses the bootstrap mean and median. Then there will follow a discussion of the comparison between normal and non-normal data in bootstrap. The study ends with a discussion and presents the advantages and disadvantages of bootstraps.

Item Type: Article
Uncontrolled Keywords: Bootstrap, Resampling, Monte Carlo
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Information and Communication Technology > Department of Industrial Computing
Depositing User: Dr. Asmala Ahmad
Date Deposited: 09 Jul 2012 06:52
Last Modified: 01 Oct 2021 12:25
URI: http://eprints.utem.edu.my/id/eprint/3613
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