Heleno Bolfarine - Regression Models for the Comparison of Measurement Methods

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In this book, we present models and statistical methods for the comparison of measurement methods problem. The need to compare measurement methods or systems, which differ in cost, speed, or other factors, has frequently appeared in various areas of knowledge, such as medicine, biology, analytical chemistry, geology, environmental sciences, psychology, and education, among many others.

In this book, we consider statistical models for continuous data, and unlike the current literature, we adopt functional measurement error models. This background includes the explicit formulation of the functional regression model and the proposal of statistical tests for detection of bias. For parameter estimation and hypothesis testing, we use the maximum likelihood method in the presence of incidental parameters and the corrected score methodology. To the best of our knowledge, both approaches have not been considered in the existing books on this topic. Furthermore, we include tools for the assessment of model adequacy and sensitivity analysis. Although these topics should be an essential part of the analytical method validation problem, they have received only scarce attention in the literature. As an essential element of the book, we furnish R code with functions implementing the techniques and examples, so that practitioners can easily analyze their own data sets.

This book is organized into five chapters. Chapter .

This book is oriented toward the needs of practitioners, laboratory scientists, applied statisticians, geostatisticians, process engineers, geologists, and analysts. It can also be a suitable book for students who specialize in the topics covered in the book.

We appreciate the support of Universidade de So Paulo, SP, Brazil (Heleno Bolfarine and Mrio de Castro) and Pontificia Universidad Catlica de Chile, Chile (Manuel Galea) for providing us with an adequate work environment for the development of our research and, in particular, for the conclusion of this book. Heleno Bolfarine and Mrio de Castro were partially supported by CNPq and FAPESP, Brazil. Manuel Galea was partially supported by grants from CONICYT and FONDECYT, Chile. We are grateful to the Editors of the SpringerBriefs in StatisticsABE Series (F. Louzada, H. S. Migon, G. A. Paula, and F. Cribari-Neto) for the opportunity to disseminate our work. We are indebted to Marcio V. de Castilho for introducing us to applications of the models discussed in this book in geology and in the mining industry. We would like to thank two anonymous reviewers, for their careful reading and valuable comments, which have led to a much improved version of the book. We also thank Robinson dos Santos and Saveetha Balasundaram, from Springer, for guiding us in the preparation of this book.