Beschreibung
Separation of signal from noise is the most fundamental problem in data analysis, arising in such fields as: signal processing, econometrics, actuarial science, and geostatistics. This book introduces the local regression method in univariate and multivariate settings, with extensions to local likelihood and density estimation. Practical information is also included on how to implement these methods in the programs S-PLUS and LOCFIT.
Inhaltsverzeichnis
Introduction.- Univariate Local Regression.- Multivariate Local Regression.- Local Likelihood Estimation.- Density Estimation.- LOCFIT: Some additional methods Survival and Failure Time Analysis.- Discrimination and Classification.- Goodness of Fit.- Bandwidth Selection.- Adaptive Parameter Choice.- Computational Methods.- Asymptotic Theory.
Autorenporträt
InhaltsangabeThe 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.
