Stochastic Complexity In Statistical Inquiry (Paperback, New edition)

This book describes how model selection and statistical inference can be founded on the shortest code length for the observed data, called the stochastic complexity. This generalization of the algorithmic complexity not only offers an objective view of statistics, where no prejudiced assumptions of 'true' data generating distributions are needed, but it also in one stroke leads to calculable expressions in a range of situations of practical interest and links very closely with mainstream statistical theory. The search for the smallest stochastic complexity extends the classical maximum likelihood technique to a new global one, in which models can be compared regardless of their numbers of parameters. The result is a natural and far reaching extension of the traditional theory of estimation, where the Fisher information is replaced by the stochastic complexity and the Cramer-Rao inequality by an extension of the Shannon-Kullback inequality. Ideas are illustrated with applications from parametric and non-parametric regression, density and spectrum estimation, time series, hypothesis testing, contingency tables, and data compression.

General

Imprint:	World Scientific Publishing Co Pte Ltd
Country of origin:	Singapore
Series:	World Scientific Series In Computer Science, 15
Release date:	August 1989
Editors:	Jorma Rissanen
Dimensions:	220mm (L)
Format:	Paperback
Pages:	188
Edition:	New edition
ISBN-13:	978-981-02-0311-5
Categories:	Books > Science & Mathematics > Mathematics > Applied mathematics > Stochastics Promotions
LSN:	981-02-0311-X
Barcode:	9789810203115

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