Overview
- Offers a sound statistical background not found in other books for the type of problems addressed, like an explicit formulation of the regression model and the proposal of the statistical test for detection of bias
- Includes comparisons of more than two methods, and analyses of model adequacy and sensitivity, topics not commonly found in the current literature
- Features R package with implementing techniques and examples to help practitioners analyze their own data sets
Part of the book series: SpringerBriefs in Statistics (BRIEFSSTATIST)
Part of the book sub series: SpringerBriefs in Statistics - ABE (BRIEFSABE)
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Table of contents (5 chapters)
Keywords
About this book
Reviews
Authors and Affiliations
About the authors
Mário de Castro is an Associate Professor at the Instituto de Ciências Matemáticas e de Computação of the Universidade de São Paulo at São Carlos, SP, Brazil. He completed his PhD studies in Statistics at the Instituto de Matemática e Estatística, Universidade de São Paulo, Brazil, and postdoctoral studies at the University of Connecticut, USA. His research interests include measurement errors, survival analysis and data modelingfor counting. He has authored or co-authored more than 60 papers.
Manuel Galea is an Associate Professor at the Pontificia Universidad Católica de Chile. He received his PhD in Statistics from the Instituto de Matemática e Estatística, Universidade de São Paulo, Brazil. His fields of research include inference and influence diagnosis in measurement error models under elliptical distributions. Dr. Galea has published more than 70 papers, as author or co-author.
Bibliographic Information
Book Title: Regression Models for the Comparison of Measurement Methods
Authors: Heleno Bolfarine, Mário de Castro, Manuel Galea
Series Title: SpringerBriefs in Statistics
DOI: https://doi.org/10.1007/978-3-030-57935-7
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
Softcover ISBN: 978-3-030-57934-0Published: 28 October 2020
eBook ISBN: 978-3-030-57935-7Published: 27 October 2020
Series ISSN: 2191-544X
Series E-ISSN: 2191-5458
Edition Number: 1
Number of Pages: X, 64
Number of Illustrations: 2 b/w illustrations, 14 illustrations in colour
Topics: Statistical Theory and Methods, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences