Nonparametric Smoothing and Lack-of-Fit Tests (Springer Series in Statistics)

Jeffrey D. Hart

ISBN 10: 0387949801 ISBN 13: 9780387949802

Editorial: Springer, 1997

Idioma: Ingl�s

Condici�n: Nuevo Encuadernaci�n de tapa dura

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Detalles bibliogr�ficos

T�tulo: Nonparametric Smoothing and Lack-of-Fit Tests (Springer Series in Statistics)
Editor: Springer
Fecha de publicaci�n: 1997
Idioma: Ingl�s
ISBN 10: 0387949801
ISBN 13: 9780387949802
Encuadernaci�n: Encuadernaci�n de tapa dura
Estado: New

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Sinopsis

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.

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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