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This book discusses the problem of model choice when the statistical models are separate, also called nonnested. Chapter 1 provides an introduction, motivating examples and a general overview of the problem. Chapter 2 presents the classical or frequentist approach to the problem as well as several alternative procedures and their properties. Chapter 3 explores the Bayesian approach, the limitations of the classical Bayes factors and the proposed alternative Bayes factors to overcome these limitations. It also discusses a significance Bayesian procedure. Lastly, Chapter 4 examines the pure likelihood approach. Various real-data examples and computer simulations are provided throughout the text.
List of contents
Preliminaries.- Frequentist Methods.- Bayesian Methods.- Support and Simulation Methods.- Maximum Likelihood Estimation.- Index.
About the author
Basilio de Bragança Pereira is a Professor of Biostatistics and of Applied Statistics at the Federal University of Rio de Janeiro in Brazil.
Carlos Alberto de Bragança Pereira is a Professor of Statistics at the University of Sao Paulo in Brazil.
Additional text
“The authors are recognized experts teaching statistics in Brazil universities, and in the book … they present various methods of choosing between competing families of regression models, for instance, exponential versus lognormal models. … The monograph is interesting, innovative, and can serve in search for adequate models in applied statistical analysis.” (Stan Lipovetsky, Technometrics, Vol. 59 (4), November, 2017)
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"The authors are recognized experts teaching statistics in Brazil universities, and in the book ... they present various methods of choosing between competing families of regression models, for instance, exponential versus lognormal models. ... The monograph is interesting, innovative, and can serve in search for adequate models in applied statistical analysis." (Stan Lipovetsky, Technometrics, Vol. 59 (4), November, 2017)
