An Introduction to Bartlett Correction and Bias Reduction
Cordeiro, Gauss M.
An Introduction to Bartlett Correction and Bias Reduction [electronic resource] / by Gauss M. Cordeiro, Francisco Cribari-Neto. - 1st ed. 2014. - XI, 107 p. online resource. - SpringerBriefs in Statistics, 2191-544X . - SpringerBriefs in Statistics, .
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.
9783642552557
10.1007/978-3-642-55255-7 doi
Statistics .
Econometrics.
Statistical Theory and Methods.
Econometrics.
Statistics for Business, Management, Economics, Finance, Insurance.
QA276-280
519.5
An Introduction to Bartlett Correction and Bias Reduction [electronic resource] / by Gauss M. Cordeiro, Francisco Cribari-Neto. - 1st ed. 2014. - XI, 107 p. online resource. - SpringerBriefs in Statistics, 2191-544X . - SpringerBriefs in Statistics, .
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.
9783642552557
10.1007/978-3-642-55255-7 doi
Statistics .
Econometrics.
Statistical Theory and Methods.
Econometrics.
Statistics for Business, Management, Economics, Finance, Insurance.
QA276-280
519.5