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

By: Contributor(s): Material type: TextTextSeries: SpringerBriefs in StatisticsPublisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2014Edition: 1st ed. 2014Description: XI, 107 p. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783642552557
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 519.5
LOC classification:
  • QA276-280
Online resources:
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 Nature eBookSummary: 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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