Bootstrap Confidence Interval for Median

Main Authors: Suprihatin, Bambang, Guritno, Suryo, Haryatmi, Sri
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Terbitan: International Statistical Institute, The Hague, The Netherlands , 2013
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Online Access: http://eprints.unsri.ac.id/4352/1/Proceeding_of_the_59th_World_Statistics_Congress%2DHong_Kong%2D2014.docx
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fullrecord <?xml version="1.0"?> <dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title>Bootstrap Confidence Interval for Median&#xD; &#xD; </title><creator>Suprihatin, Bambang</creator><creator>Guritno, Suryo</creator><creator>Haryatmi, Sri</creator><subject>QA Mathematics</subject><description>. Given sample of size n from an unknown distribution F. If all elements of X are distinct, then the number of different possible resamples with replacement equals . In general, this number obvious very large in amount. For n = 10, think of the number , which is an enormous number. Let be the estimate value of statistic computed from , where t is functional. In most cases of practical interest, each distinct (without regard for order), gives rise to a distinct . Accordingly, we concern only on so-called atoms of nonparametric bootstrap. The number of atoms is far less than Based on these atoms, the nonparametric bootstrap used to estimate a statistic computed from X. This paper presents how to find the number of atoms. The implementation of the uses of atoms is applied in bootstrapping bias estimate of sample median. Bootstrap version of standar error as a measure of accuracy of estimator is considered, as well. The main purpose of this paper is to construct a confidence interval for median. Results from Monte Carlo simulation for these cases are also presented.</description><publisher>International Statistical Institute, The Hague, The Netherlands</publisher><date>2013</date><type>Book:BookSection</type><type>PeerReview:PeerReviewed</type><type>File:application/msword</type><identifier>http://eprints.unsri.ac.id/4352/1/Proceeding_of_the_59th_World_Statistics_Congress%2DHong_Kong%2D2014.docx</identifier><identifier>Suprihatin, Bambang and Guritno, Suryo and Haryatmi, Sri (2013) Bootstrap Confidence Interval for Median. In: Proceedings of the 59th World Statistics Congress of the International Statistical Institute. International Statistical Institute, The Hague, The Netherlands. ISBN 978-90-73592-34-6</identifier><relation>http://eprints.unsri.ac.id/4352/</relation><recordID>4352</recordID></dc>
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author Suprihatin, Bambang
Guritno, Suryo
Haryatmi, Sri
title Bootstrap Confidence Interval for Median
publisher International Statistical Institute, The Hague, The Netherlands
publishDate 2013
isbn 9789073592346
topic QA Mathematics
url http://eprints.unsri.ac.id/4352/1/Proceeding_of_the_59th_World_Statistics_Congress%2DHong_Kong%2D2014.docx
http://eprints.unsri.ac.id/4352/
contents . Given sample of size n from an unknown distribution F. If all elements of X are distinct, then the number of different possible resamples with replacement equals . In general, this number obvious very large in amount. For n = 10, think of the number , which is an enormous number. Let be the estimate value of statistic computed from , where t is functional. In most cases of practical interest, each distinct (without regard for order), gives rise to a distinct . Accordingly, we concern only on so-called atoms of nonparametric bootstrap. The number of atoms is far less than Based on these atoms, the nonparametric bootstrap used to estimate a statistic computed from X. This paper presents how to find the number of atoms. The implementation of the uses of atoms is applied in bootstrapping bias estimate of sample median. Bootstrap version of standar error as a measure of accuracy of estimator is considered, as well. The main purpose of this paper is to construct a confidence interval for median. Results from Monte Carlo simulation for these cases are also presented.
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