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dc.contributor.authorSantana, Leonard
dc.contributor.authorSwanepoel, Jan W.H.
dc.date.accessioned2018-05-08T11:11:13Z
dc.date.available2018-05-08T11:11:13Z
dc.date.issued2018
dc.identifier.citationSantana, L. & Swanepoel, J.W.H. 2018. A nonparametric point estimation technique using the m-out-of-n bootstrap. South African statistical journal, 52(1):73-92. [http://hdl.handle.net/10520/EJC-da6307bf9]en_US
dc.identifier.issn0038-271X
dc.identifier.issn1996-8450 (Online)
dc.identifier.urihttp://hdl.handle.net/10394/26830
dc.identifier.urihttp://hdl.handle.net/10520/EJC-da6307bf9
dc.identifier.urihttps://journals.co.za/content/journal/10520/EJC-da6307bf9
dc.description.abstractWe investigate a method which can be used to improve an existing point estimator by a modification of the estimator and by using the m-out-of-n bootstrap. The estimation method used, known as bootstrap robust aggregating (or BRAGGing) in the literature, will be applied in general to the estimators that satisfy the smooth function model (for example, a mean, a variance, a ratio of means or variances, or a correlation coefficient), and then specifically to an estimator for the population mean. BRAGGing estimators based on both a naive and corrected version of them-out-of-n bootstrap will be considered. We conclude with proposed data-based choices of the resample size, m, as well as Monte-Carlo studies illustrating the performance of the estimators when estimating the population mean for various distributionsen_US
dc.language.isoenen_US
dc.publisherSASAen_US
dc.subjectBRAGGingen_US
dc.subjectCornish-Fisher expansionen_US
dc.subjectm-out-of-n bootstrapen_US
dc.subjectMonte-Carlo studyen_US
dc.subjectPoint estimationen_US
dc.subjectResample sizeen_US
dc.subjectSmooth function modelen_US
dc.titleA nonparametric point estimation technique using the m-out-of-n bootstrapen_US
dc.typeArticleen_US
dc.contributor.researchID11803371 - Santana, Leonard
dc.contributor.researchID10177507 - Swanepoel, Jan Willem Hendrik


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