Nonparametric statistics for stochastic processes : estimation and prediction
Author(s)
Bibliographic Information
Nonparametric statistics for stochastic processes : estimation and prediction
（Lecture notes in statistics, v. 110）
Springer-Verlag, c1996
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
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.
Table of Contents
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.
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