T�tulo: Nonparametric Smoothing and Lack-of-Fit Tests (Springer Series in Statistics)
Editor: Springer
Fecha de publicaci�n: 2012
Idioma: Ingl�s
ISBN 10: 1475727240
ISBN 13: 9781475727241
Encuadernaci�n: Encuadernaci�n de tapa blanda
Estado: New

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Sinopsis

This book is an essential reference for applied and theoretical statisticians interested in the theory and applications of fitting probability models to data.

Rese�a del editor

An exploration of the use of smoothing methods in testing the fit of parametric regression models. The book reviews many of the existing methods for testing lack-of-fit and also proposes a number of new methods, addressing both applied and theoretical aspects of the model checking problems. As such, the book is of interest to practitioners of statistics and researchers investigating either lack-of-fit tests or nonparametric smoothing ideas. The first four chapters introduce the problem of estimating regression functions by nonparametric smoothers, primarily those of kernel and Fourier series type, and could be used as the foundation for a graduate level course on nonparametric function estimation. The prerequisites for a full appreciation of the book are a modest knowledge of calculus and some familiarity with the basics of mathematical statistics.

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