Local Regression and Likelihood
(Sprache: Englisch)
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...
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Klappentext zu „Local Regression and Likelihood “
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.
Inhaltsverzeichnis zu „Local Regression and Likelihood “
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.
Autoren-Porträt von Clive Loader
Smoothing methods play an important role in many areas of statistics. This book explains how to implement these methods in several popular statistical programs including S-PLUS.
Bibliographische Angaben
- Autor: Clive Loader
- 2013, Softcover reprint of the original 1st ed. 1999, 290 Seiten, Maße: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 1475772580
- ISBN-13: 9781475772586
Sprache:
Englisch
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