Regression Models for the Comparison of Measurement Methods

1. Front Matter

   Pages i-x

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2. Introduction

   • Heleno Bolfarine, Mário de Castro, Manuel Galea

   Pages 1-4

3. Two Methods

   • Heleno Bolfarine, Mário de Castro, Manuel Galea

   Pages 5-15

4. Two or More Methods

   • Heleno Bolfarine, Mário de Castro, Manuel Galea

   Pages 17-25

5. Model Checking and Influence Assessment

   • Heleno Bolfarine, Mário de Castro, Manuel Galea

   Pages 27-35

6. Data Analysis

   • Heleno Bolfarine, Mário de Castro, Manuel Galea

   Pages 37-48

7. Back Matter

   Pages 49-64

   PDF

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, laboratoryscientists, geostatisticians, process engineers, geologists and graduate students.

• regression models
• measurement methods
• bias detection
• method validation
• agreement

“This book is a successful compilation of such developments in the last two decades and presents them concisely to help researchers and practitioners. … The book requires the reader to have a solid background in mathematical statistics and detailed knowledge of areas such as measurement error models. It also provides a platform for new entrants in this area to begin their research with complete references and updated developments in one place.” (Shalabh, Mathematical Reviews, April, 2022)

• Department of Statistics, University of São Paulo, Sao Paulo, Brazil

  Heleno Bolfarine

• Department of Applied Mathematics and Statistics, University of São Paulo, São Carlos, Brazil

  Mário de Castro

• Department of Statistics, Pontificia Universidad Católica de Chile, Santiago, Chile

  Manuel Galea

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 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.

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