Information Criteria and Statistical Modeling
Seiten
2010
Springer-Verlag New York Inc.
978-1-4419-2456-8 (ISBN)
Springer-Verlag New York Inc.
978-1-4419-2456-8 (ISBN)
This brilliantly structured and comprehensive volume provides exhaustive explanations of the concepts and philosophy of statistical modeling, together with a wide range of practical and numerical examples.
The Akaike information criterion (AIC) derived as an estimator of the Kullback-Leibler information discrepancy provides a useful tool for evaluating statistical models, and numerous successful applications of the AIC have been reported in various fields of natural sciences, social sciences and engineering.
One of the main objectives of this book is to provide comprehensive explanations of the concepts and derivations of the AIC and related criteria, including Schwarz’s Bayesian information criterion (BIC), together with a wide range of practical examples of model selection and evaluation criteria. A secondary objective is to provide a theoretical basis for the analysis and extension of information criteria via a statistical functional approach. A generalized information criterion (GIC) and a bootstrap information criterion are presented, which provide unified tools for modeling and model evaluation for a diverse range of models, including various types of nonlinear models and model estimation procedures such as robust estimation, the maximum penalized likelihood method and a Bayesian approach.
The Akaike information criterion (AIC) derived as an estimator of the Kullback-Leibler information discrepancy provides a useful tool for evaluating statistical models, and numerous successful applications of the AIC have been reported in various fields of natural sciences, social sciences and engineering.
One of the main objectives of this book is to provide comprehensive explanations of the concepts and derivations of the AIC and related criteria, including Schwarz’s Bayesian information criterion (BIC), together with a wide range of practical examples of model selection and evaluation criteria. A secondary objective is to provide a theoretical basis for the analysis and extension of information criteria via a statistical functional approach. A generalized information criterion (GIC) and a bootstrap information criterion are presented, which provide unified tools for modeling and model evaluation for a diverse range of models, including various types of nonlinear models and model estimation procedures such as robust estimation, the maximum penalized likelihood method and a Bayesian approach.
Concept of Statistical Modeling.- Statistical Models.- Information Criterion.- Statistical Modeling by AIC.- Generalized Information Criterion (GIC).- Statistical Modeling by GIC.- Theoretical Development and Asymptotic Properties of the GIC.- Bootstrap Information Criterion.- Bayesian Information Criteria.- Various Model Evaluation Criteria.
| Erscheint lt. Verlag | 23.11.2010 |
|---|---|
| Reihe/Serie | Springer Series in Statistics |
| Zusatzinfo | XII, 276 p. |
| Verlagsort | New York, NY |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Informatik ► Grafik / Design ► Digitale Bildverarbeitung | |
| Informatik ► Theorie / Studium ► Kryptologie | |
| Mathematik / Informatik ► Mathematik ► Angewandte Mathematik | |
| Mathematik / Informatik ► Mathematik ► Statistik | |
| Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik | |
| Naturwissenschaften ► Biologie | |
| Wirtschaft ► Volkswirtschaftslehre ► Ökonometrie | |
| ISBN-10 | 1-4419-2456-6 / 1441924566 |
| ISBN-13 | 978-1-4419-2456-8 / 9781441924568 |
| Zustand | Neuware |
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