Information criteria and statistical modeling

By:

Konishi, Sadanori

Material type: Text

TextPublication details: 2008 Springer Science+Business Media New YorkDescription: xii, 273 p. : ill. ; 24 cmISBN:

9780387718866

Subject(s):

DDC classification:

519.22 K6I6

Summary: 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. (http://www.springer.com/statistics/statistical+theory+and+methods/book/978-0-387-71886-6)

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Holdings
Item type	Current library	Call number	Status	Date due	Barcode	Item holds
Book	Ahmedabad	519.22 K6I6 (Browse shelf(Opens below))	Available		173700

Total holds: 0

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. (http://www.springer.com/statistics/statistical+theory+and+methods/book/978-0-387-71886-6)

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