The Performance of Double Bootstrap Method for Large Sampling Sequence

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dc.contributor.authorMuhamad Safiih Lola
dc.contributor.authorNurul Hila Zainuddin
dc.date.accessioned2017-04-10T07:30:51Z
dc.date.available2017-04-10T07:30:51Z
dc.date.issued2016-10
dc.description.abstractStudies on the iteration procedure in double bootstrap method have given a great impact on confidence interval performance. However, the procedure was claimed to be complicated and demand intensive computer processor. Considering this problem, an alternative procedure was proposed in this research. Despite of using small sampling sequence, this research was aimed to increase the accuracy estimation using a second replication number which resulted in a large sampling sequence of double bootstrap. In this paper, the alternative double bootstrap method was hybrid onto an example model and its performance was based on Studentised interval. The performance was examined in simulation study and real sample data of sukuk Ijarah. The result showed that hybrid double bootstrap model gave more accurate estimation in terms of its shorter length when dealing with various parameter values and has shown to improve the single bootstrap estimation.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/5566
dc.language.isoenen_US
dc.publisherOpen Journal of Statisticsen_US
dc.subjectDouble Bootstrapen_US
dc.subjectConfidence Intervalen_US
dc.subjectSampling Sequenceen_US
dc.subjectEWMAen_US
dc.subjectSukuk Ijarahen_US
dc.titleThe Performance of Double Bootstrap Method for Large Sampling Sequenceen_US
dc.typeArticleen_US
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