Local Regression and Likelihood (Statistics and Computing)

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. "This book provides an overview of the theory, methods, and application of local regression and likelihood. The first five chapters introduce the problems, first in the local regression setting, followed by extensions to likelihood-based regression models and density estimation. The remaining chapters cover a range of advanced topics and applications, including robust smoothing, survival analysis, classification, and model selection issues."--BOOK JACKET. 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.