Nonparametric Smoothing and Lack-of-Fit Tests

Jeffrey Hart

ISBN 10: 0387949801 / ISBN 13: 9780387949802

Edité par Springer New York, Springer US Jul 1997, 1997

A propos de cet article

Neuware -The The primary primary aim aim of of this this book book is is to to explore explore the the use use of of nonparametric nonparametric regres regres sion sion (i. e. , (i. e. , smoothing) smoothing) methodology methodology in in testing testing the the fit fit of of parametric parametric regression regression models. models. It It is is anticipated anticipated that that the the book book will will be be of of interest interest to to an an audience audience of of graduate graduate students, students, researchers researchers and and practitioners practitioners who who study study or or use use smooth smooth ing ing methodology. methodology. Chapters Chapters 2-4 2-4 serve serve as as a a general general introduction introduction to to smoothing smoothing in in the the case case of of a a single single design design variable. variable. The The emphasis emphasis in in these these chapters chapters is is on on estimation estimation of of regression regression curves, curves, with with hardly hardly any any mention mention of of the the lack-of lack-of fit fit problem. problem. As As such, such, Chapters Chapters 2-4 2-4 could could be be used used as as the the foundation foundation of of a a graduate graduate level level statistics statistics course course on on nonparametric nonparametric regression. regression.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 306 pp. Englisch.

N° de réf. du vendeur 9780387949802

Détails bibliographiques

• Titre: Nonparametric Smoothing and Lack-of-Fit Tests
• Éditeur: Springer New York, Springer US Jul 1997
• Date de parution: 1997
• Langue: anglais
• ISBN à 10 chiffres: 0387949801
• ISBN à 13 chiffres: 9780387949802
• Reliure: Buch
• État de l'article: Neu

A propos de ce titre

Synopsis

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