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
Bibliography
Includes bibliographical references (pages 168-175) and index
