Gauss M. Cordeiro, Francisco Cribari-Neto

An Introduction to Bartlett Correction and Bias Reduction (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

Produktdetails

• Verlag: Springer Berlin
• Erscheinungstermin: 8. Mai 2014
• Englisch
• ISBN-13: 9783642552557
• Artikelnr.: 43783549

Autorenporträt

Gauss M. Cordeiro is a Professor of Statistics at Universidade Federal de Pernambuco in Brazil. He is a former chief editor of the Brazilian Journal of Probability and Statistics and a former president of the Brazilian Statistical Association.

Francisco Cribari-Neto is a Professor of Statistics at Universidade Federal de Pernambuco in Brazil. He is a former applications editor of the Brazilian Journal of Probability and Statistics and a former president of the Brazilian Econometric Society.

Inhaltsangabe

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.

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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.

Rezensionen

From the book reviews:

"This monograph endeavors to give a review of research on the topic of Bartlett and Bartlett-type corrections that can be applied to test statistics as well as bias corrections of maximum likelihood estimators. The authors have written an interesting book, which is intended to serve the need of researchers and graduate students in statistics. The book could also be very useful as a supplement for graduate level courses among others in statistical inference." (Apostolos Batsidis, zbMATH 1306.62025, 2015)