COMPARISON OF BOOTSTRAP AND JACKKNIFE VARIANCE ESTIMATORS IN LINEAR REGRESSION: SECOND ORDER RESULTS

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dc.contributor.authorBose, Arup
dc.contributor.authorChatterjee, Snigdhansu
dc.date.accessioned2026-03-05T19:35:53Z
dc.date.issued2002
dc.description.abstractIn an extension of the work of Liu and Singh (1992), we consider resampling estimates for the variance of the least squares estimator in linear regression models. Second order terms in asymptotic expansions of these estimates are derived. By comparing the second order terms, certain generalised bootstrap schemes are seen to be theoretically better than other resampling techniques under very general conditions. The performance of the different resampling schemes are studied through a few simulations.
dc.description.urihttps://www3.stat.sinica.edu.tw/statistica/oldpdf/A12n212.pdf
dc.format.extent24 pages
dc.genrejournal articles
dc.identifierdoi:10.13016/m2j0a5-2ymm
dc.identifier.citationBose, Arup, and Snigdhansu Chatterjee. “COMPARISON OF BOOTSTRAP AND JACKKNIFE VARIANCE ESTIMATORS IN LINEAR REGRESSION: SECOND ORDER RESULTS.” Statistica Sinica 12 (2002): 575–98.
dc.identifier.urihttp://hdl.handle.net/11603/42038
dc.language.isoen
dc.publisherInstitute of Statistical Science, Academia Sinica
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Mathematics and Statistics Department
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.titleCOMPARISON OF BOOTSTRAP AND JACKKNIFE VARIANCE ESTIMATORS IN LINEAR REGRESSION: SECOND ORDER RESULTS
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0002-7986-0470

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