Information Criteria and Statistical Modeling

• 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.

From the Reviews: "I was fully satisfied with it. The authors are obviously well-qualified to write on the subject." (Biometrics Book Reviews, 2008) "This book explains the basic ideas of model evaluation and presents the definition and derivation of the AIC and related criteria, including BIC. ... The book makes a major contribution to the understanding of statistical modeling. Researchers interested in statistical modeling will find a lot of interesting material in it."(Erkki P. Liski, International Statistical Reviews, Vol. 76 (2), 2008) "...Modeling is an important and challenging endeavor that permeates nearly all aspects of applied statistics. The validity of inferences, predictions, and conclusions depends on the propriety of the model serving as their basis. Any book that improves the ability of practicing statisticians and biostatisticians to formulate, select and use models is worth its weight in gold. Konishi and Kitagawa have written such a book." (Journal of the American Statistical Association September 2009, Vol. 104, No. 487, Book Reviews) "With the main purpose of explaining the critical role of information criteria in statistical modeling, this book is written by two leading experts. ... The book ends with a list of references and an index. The style of writing is very good. Examples illustrate the concepts discussed and make the book immensely readable. ... Anybody interested in statistical modeling will love to read this book. ... it will be very useful to researchers and students interested in learning statistical modeling and model evaluation." (Ravi Sreenivasan, Zentralblatt MATH, Vol. 1172, 2009)