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Model Choice in Nonnested Families [electronic resource] / by Basilio de Bragança Pereira, Carlos Alberto de Bragança Pereira.

By: Contributor(s): Material type: TextTextSeries: SpringerBriefs in StatisticsPublisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2016Description: X, 96 p. 5 illus., 2 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783662537367
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 519.5 23
LOC classification:
  • QA276-280
Online resources:
Contents:
Preliminaries -- Frequentist Methods -- Bayesian Methods -- Support and Simulation Methods -- Maximum Likelihood Estimation -- Index.
In: Springer eBooksSummary: 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.
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Preliminaries -- Frequentist Methods -- Bayesian Methods -- Support and Simulation Methods -- Maximum Likelihood Estimation -- Index.

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.

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