Local Regression and Likelihood / Edition 1

by Catherine Loader

Overview

Separation of signal from noise is the most fundamental problem in data analysis, and arises in many fields, for example, signal processing, econometrics, acturial science, and geostatistics. This book introduces the local regression method in univariate and multivariate settings, and extensions to local likelihood and density estimation. Basic theoretical results and diagnostic tools such as cross validation are introduced along the way. Examples illustrate the implementation of the methods using the LOCFIT software.

Product Details

ISBN-13:	9780387987750
Publisher:	Springer New York
Publication date:	07/30/1999
Series:	Statistics and Computing
Edition description:	1999
Pages:	290
Product dimensions:	6.10(w) x 9.25(h) x (d)

Table of Contents

The Origins of Local Regression.- Local Regression Methods.- Fitting with LOCFIT.- Local Likelihood Estimation.- Density Estimation.- Flexible Local Regression.- Survival and Failure Time Analysis.- Discrimination and Classification.- Variance Estimation and Goodness of Fit.- Bandwidth Selection.- Adaptive Parameter Choice.- Computational Methods.- Optimizing Local Regression.