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Stochastic complexity in statistical inquiry [electronic resource] / Jorma Rissanen.

By: Rissanen, Jorma.
Material type: TextTextSeries: World Scientific series in computer science: v. 15.Publisher: Singapore ; River Edge, NJ : World Scientific, c1989Description: 1 online resource (x, 177 p.) : ill.ISBN: 9812385495 (electronic bk.); 9789812385499 (electronic bk.).Subject(s): Machine theory | Computational complexity | Stochastic processes | MATHEMATICS -- Infinity | MATHEMATICS -- Logic | Automates math�ematiques, Th�eorie des | Complexit�e de calcul (Informatique) | Processus stochastiquesGenre/Form: Electronic books.Additional physical formats: Print version:: Stochastic complexity in statistical inquiry.DDC classification: 511.3 Online resources: EBSCOhost
Contents:
1 INTRODUCTION; 2 MODELS, CODES, AND COMPLEXITY; 3 STOCHASTIC COMPLEXITY; 4 MODEL VALIDATION; 5 LINEAR REGRESSION; 6 TIME SERIES; 7 APPLICATIONS;
Summary: 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 likelih.
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Includes bibliographical references (p. 168-175) and index.

Description based on print version record.

1 INTRODUCTION; 2 MODELS, CODES, AND COMPLEXITY; 3 STOCHASTIC COMPLEXITY; 4 MODEL VALIDATION; 5 LINEAR REGRESSION; 6 TIME SERIES; 7 APPLICATIONS;

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 likelih.

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