A Distribution-Free Theory of Nonparametric Regression - Couverture rigide
Livre 121 sur 160: Springer Series in Statistics
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.
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A Distribution-Free Theory of Nonparametric Regression (Springer Series in Statistics)
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A Distribution-Free Theory of Nonparametric Regression
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Springer New York, 2002
ISBN 10 : 0387954414
ISBN 13 : 9780387954417
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Couverture rigide
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A Distribution-Free Theory of Nonparametric Regression
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Springer New York, 2002
ISBN 10 : 0387954414
ISBN 13 : 9780387954417
Neuf
Couverture rigide
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.  This book provides a systematic in-depth analysis of nonparametric regression with random design. It covers almost all known estimates. The emphasis is on distribution-free properties of the estimates. |The regression estimation problem has a l. N° de réf. du vendeur 5912533
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A Distribution-Free Theory of Nonparametric Regression
Edité par
Springer New York, Springer US Aug 2002, 2002
ISBN 10 : 0387954414
ISBN 13 : 9780387954417
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - 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. 672 pp. Englisch. N° de réf. du vendeur 9780387954417
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A Distribution-Free Theory of Nonparametric Regression
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Buch. Etat : Neu. A Distribution-Free Theory of Nonparametric Regression | László Györfi (u. a.) | Buch | xvi | Englisch | 2002 | Humana | EAN 9780387954417 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. N° de réf. du vendeur 102997708
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A Distribution-Free Theory of Nonparametric Regression
Edité par
Springer New York, Springer US Aug 2002, 2002
ISBN 10 : 0387954414
ISBN 13 : 9780387954417
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Buch. Etat : Neu. 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
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A Distribution-Free Theory of Nonparametric Regression
Edité par
Springer New York, Springer US, 2002
ISBN 10 : 0387954414
ISBN 13 : 9780387954417
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. N° de réf. du vendeur 9780387954417
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