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An introduction to Bartlett correction and bias reduction
This book presents a concise introduction to Bartlett and Bartlett-type corrections of statistical tests and bias correction of point estimators. The underlying idea behind both groups of corrections is to obtain higher accuracy in small samples. While the main focus is on corrections that can be an...
Autores principales: | , |
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Lenguaje: | eng |
Publicado: |
Springer
2014
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1007/978-3-642-55255-7 http://cds.cern.ch/record/1707525 |
_version_ | 1780936548157489152 |
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author | Cordeiro, Gauss M Cribari-Neto, Francisco |
author_facet | Cordeiro, Gauss M Cribari-Neto, Francisco |
author_sort | Cordeiro, Gauss M |
collection | CERN |
description | This book presents a concise introduction to Bartlett and Bartlett-type corrections of statistical tests and bias correction of point estimators. The underlying idea behind both groups of corrections is to obtain higher accuracy in small samples. While the main focus is on corrections that can be analytically derived, the authors also present alternative strategies for improving estimators and tests based on bootstrap, a data resampling technique, and discuss concrete applications to several important statistical models. |
id | cern-1707525 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2014 |
publisher | Springer |
record_format | invenio |
spelling | cern-17075252021-04-21T20:58:34Zdoi:10.1007/978-3-642-55255-7http://cds.cern.ch/record/1707525engCordeiro, Gauss MCribari-Neto, FranciscoAn introduction to Bartlett correction and bias reductionMathematical Physics and MathematicsThis book presents a concise introduction to Bartlett and Bartlett-type corrections of statistical tests and bias correction of point estimators. The underlying idea behind both groups of corrections is to obtain higher accuracy in small samples. While the main focus is on corrections that can be analytically derived, the authors also present alternative strategies for improving estimators and tests based on bootstrap, a data resampling technique, and discuss concrete applications to several important statistical models.Springeroai:cds.cern.ch:17075252014 |
spellingShingle | Mathematical Physics and Mathematics Cordeiro, Gauss M Cribari-Neto, Francisco An introduction to Bartlett correction and bias reduction |
title | An introduction to Bartlett correction and bias reduction |
title_full | An introduction to Bartlett correction and bias reduction |
title_fullStr | An introduction to Bartlett correction and bias reduction |
title_full_unstemmed | An introduction to Bartlett correction and bias reduction |
title_short | An introduction to Bartlett correction and bias reduction |
title_sort | introduction to bartlett correction and bias reduction |
topic | Mathematical Physics and Mathematics |
url | https://dx.doi.org/10.1007/978-3-642-55255-7 http://cds.cern.ch/record/1707525 |
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