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. This Book Provides A Mathematically Rigorous Treatment Of The Theory Of Nonparametric Estimation And Prediction For Stochastic Processes. It Discusses Discrete Time And Continuous Time, And The Emphasis Is On The Kernel Methods. Several New Results Are Presented Concerning Optimal And Superoptimal Convergence Rates. How To Implement The Method Is Discussed In Detail And Several Numerical Results Are Presented. This Book Will Be Of Interest To Specialists In Mathematical Statistics And To Those Who Wish To Apply These Methods To Practical Problems Involving Time Series Analysis. Contents: Synopsis -- Inequalities For Mixing Processes -- Density Estimation For Discrete Time Processes -- Regression Estimation And Prediction For Discrete Time Processes -- Density Estimation For Continuous Time Processes -- Regression Estimates And Prediction In Continuous Time -- Appendix -- Bibliography -- Index. D. Bosq. Includes Bibliographical References And Index. This text is devoted to the theory and applications of nonparametic functional estimation and prediction. Chapter One provides an overview of inequalities and limit theorems for strong mixing processes. Density and regression estimation in discrete time are studies in Chapter Two and Three. The special rates of convergence which appear in continuous time are presented in Chapters Four and Five. This second edition is e×tensively revised and it contains two new chapters. Chapter Si× discusses the surprising local time density estimator. Chapter Seven gives a detailed account of implementation of nonparametric method and practical e×amples in economics, finance and physics. Comparison with ARMA and ARCH methods shows the efficiency of nonparametric forecasting. The prerequisite is a knowledge of classical probability theory and statistics Front Matter....Pages i-xvi Synopsis....Pages 1-15 Inequalities for mixing processes....Pages 17-39 Density estimation for discrete time processes....Pages 41-65 Regression estimation and prediction for discrete time processes....Pages 67-87 Kernel density estimation for continuous time processes....Pages 89-128 Regression estimation and prediction in continuous time....Pages 129-144 The local time density estimator....Pages 145-167 Implementation of nonparametric method and numerical applications....Pages 169-195 Back Matter....Pages 197-212 This book is devoted to the theory and applications of nonparametric functional estimation and prediction. The second edition is extensively revised and contains two new chapters. One discusses the surprising local time density estimator. The other gives a detailed account of the implementation of nonparametric methods and practical examples in economics, finance, and physics. A comparison with ARMA and ARCH methods shows the efficiency of nonparametric forecasting. The book assumes a knowledge of classical probability theory and statistics. Deals with the theory and applications of nonparametic functional estimation and prediction. This book provides an overview of inequalities and limit theorems for strong mixing processes. It studies density and regression estimation in discrete time. It presents the special rates of convergence which appear in continuous time. This work discusses discrete time and continuous time, with emphasis on the kernel methods. Recent results concerning optimal and superoptimal convergence rates are presented, and the implementation of the method is discussed.
