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An Introduction to Bartlett Correction and Bias Reduction electronic resource by Gauss M. Cordeiro, Francisco Cribari-Neto.

By: Cordeiro, Gauss M [author.]Contributor(s): Cribari-Neto, Francisco [author.] | SpringerLink (Online service)Material type: TextTextSeries: SpringerBriefs in StatisticsPublication details: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2014Description: XI, 107 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783642552557Subject(s): Statistics | Mathematical statistics | Economics -- Statistics | Econometrics | Statistics | Statistical Theory and Methods | Econometrics | Statistics for Business/Economics/Mathematical Finance/InsuranceDDC classification: 519.5 LOC classification: QA276-280Online resources: Click here to access online
Contents:
Preface -- Likelihood-Based Inference and Finite-Sample Corrections: A Brief Overview -- Bartlett Corrections and Bootstrap Testing Inference -- Bartlett-Type Corrections -- Analytical and Bootstrap Bias Corrections -- Supplementary Material -- Glossary.
In: Springer eBooksSummary: 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.
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Preface -- Likelihood-Based Inference and Finite-Sample Corrections: A Brief Overview -- Bartlett Corrections and Bootstrap Testing Inference -- Bartlett-Type Corrections -- Analytical and Bootstrap Bias Corrections -- Supplementary Material -- Glossary.

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.

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