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Nonparametric Statistics for Stochastic Processes

Estimation and Prediction

Lecture Notes in Statistics Band 110

D. Bosq

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Beschreibung

This book is devoted to the theory and applications of nonparametic functional estimation and prediction. Chapter 1 provides an overview of inequalities and limit theorems for strong mixing processes. Density and regression estimation in discrete time are studied in Chapter 2 and 3. The special rates of convergence which appear in continuous time are presented in Chapters 4 and 5. This second edition is extensively revised and it contains two new chapters. Chapter 6 discusses the surprising local time density estimator. Chapter 7 gives a detailed account of implementation of nonparametric method and practical examples in economics, finance and physics. Comarison with ARMA and ARCH methods shows the efficiency of nonparametric forecasting. The prerequisite is a knowledge of classical probability theory and statistics. Denis Bosq is Professor of Statistics at the Unviersity of Paris 6 (Pierre et Marie Curie). He is Editor-in-Chief of "Statistical Inference for Stochastic Processes" and an editor of "Journal of Nonparametric Statistics". He is an elected member of the International Statistical Institute. He has published about 90 papers or works in nonparametric statistics and four books.

Produktdetails

Einband Taschenbuch
Seitenzahl 232
Erscheinungsdatum 13.08.1998
Sprache Englisch
ISBN 978-0-387-98590-9
Verlag Springer US
Maße (L/B/H) 23,5/15,5/1,2 cm
Gewicht 380 g
Auflage 2nd ed. 1998

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  • Synopsis.- 1. Inequalities for mixing processes.- 2. Density estimation for discrete time processes.- 3. Regression estimation and prediction for discrete time processes.- 4. Kernel density estimation for continuous time processes.- 5. Regression estimation and prediction in continuous time.- 6. The local time density estimator.- 7. Implementation of nonparametric method and numerical applications.- References.