SpringerBriefs in Statistics - ABE

Regression Models for the Comparison of Measurement Methods

Authors: Bolfarine, Heleno, De Castro Andrade Filho, Mario, Galea, Manuel

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  • 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
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eBook 42,79 €
price for Spain (gross)
  • ISBN 978-3-030-57935-7
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  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
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Softcover 51,99 €
price for Spain (gross)
About this book

This book provides an updated account of the regression techniques employed in comparing analytical methods and to test the biases of one method relative to others – a problem commonly found in fields like analytical chemistry, biology, engineering, and medicine. Methods comparison involves a non-standard regression problem; when a method is to be tested in a laboratory, it may be used on samples of suitable reference material, but frequently it is used with other methods on a range of suitable materials whose concentration levels are not known precisely. By presenting a sound statistical background not found in other books for the type of problem addressed, this book complements and extends topics discussed in the current literature. It highlights the applications of the presented techniques with the support of computer routines implemented using the R language, with examples worked out step-by-step. This book is a valuable resource for applied statisticians, practitioners, laboratory scientists, geostatisticians, process engineers, geologists and graduate students.

About the authors

Heleno Bolfarine is a Full Professor at the Instituto de Matemática e Estatística of the Universidade de São Paulo, SP, Brazil. He received his PhD in Probability and Statistics from the University of California at Berkeley, USA. Prof. Bolfarine has published more than 190 articles in respected ,international, peer-reviewed journals, and co-authored the book “Prediction Theory for Finite Populations”, published by Springer. His research focuses on statistical inference, more specifically on mixed models and finite populations.
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 modeling for 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.

Table of contents (5 chapters)

Table of contents (5 chapters)

Buy this book

eBook 42,79 €
price for Spain (gross)
  • ISBN 978-3-030-57935-7
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover 51,99 €
price for Spain (gross)
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Bibliographic Information

Bibliographic Information
Book Title
Regression Models for the Comparison of Measurement Methods
Authors
Series Title
SpringerBriefs in Statistics - ABE
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
eBook ISBN
978-3-030-57935-7
DOI
10.1007/978-3-030-57935-7
Softcover ISBN
978-3-030-57934-0
Series ISSN
2524-6917
Edition Number
1
Number of Pages
X, 64
Number of Illustrations
2 b/w illustrations, 14 illustrations in colour
Topics