A Distribution-Free Theory of Nonparametric Regression

László Györfi

ISBN 10: 0387954414 / ISBN 13: 9780387954417

Edité par Springer New York, Springer US Aug 2002, 2002

A propos de cet article

Neuware -The regression estimation problem has a long history. Already in 1632 Galileo Galilei used a procedure which can be interpreted as tting a linear relationship to contaminated observed data. Such tting of a line through a cloud of points is the classical linear regression problem. A solution of this problem is provided by the famous principle of least squares, which was discovered independently by A. M. Legendre and C. F. Gauss and published in 1805 and 1809, respectively. The principle of least squares can also be applied to construct nonparametric regression estimates, where one does not restrict the class of possible relationships, and will be one of the approaches studied in this book. Linear regression analysis, based on the concept of a regression function, was introduced by F. Galton in 1889, while a probabilistic approach in the context of multivariate normal distributions was already given by A. B- vais in 1846. The rst nonparametric regression estimate of local averaging type was proposed by J. W. Tukey in 1947. The partitioning regression - timate he introduced, by analogy to the classical partitioning (histogram) density estimate, can be regarded as a special least squares estimate.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 672 pp. Englisch.

N° de réf. du vendeur 9780387954417

Détails bibliographiques

• Titre: A Distribution-Free Theory of Nonparametric Regression
• Éditeur: Springer New York, Springer US Aug 2002
• Date de parution: 2002
• Langue: anglais
• ISBN à 10 chiffres: 0387954414
• ISBN à 13 chiffres: 9780387954417
• Reliure: Buch
• État de l'article: Neu

A propos de ce titre

Synopsis

This monograph presents a modern approach to nonparametric regression with random design. The audience is graduate students and researchers in statistics, mathematics, computer science, and engineering.