Information criteria and statistical modeling
Konishi, Sadanori
Information criteria and statistical modeling - New York Springer Science+Business Media 2008 - xii, 273 p. : ill. ; 24 cm.
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)
9780387718866
Information modeling
Mathematical analysis
Stochastic analysis
519.22 / K6I6
Information criteria and statistical modeling - New York Springer Science+Business Media 2008 - xii, 273 p. : ill. ; 24 cm.
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)
9780387718866
Information modeling
Mathematical analysis
Stochastic analysis
519.22 / K6I6