9780387954417 - a distribution-free theory of nonparametric regression (springer series in statistics) de krzyzak, adam; gyorfi, laszlo; kohler, michael (10 resultados)
Idioma: Inglés
Editorial: Springer, 2002
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Librería: Studibuch, Stuttgart, AlemaniaStudibuch
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hardcover. Condición: Sehr gut. 666 Seiten; 9780387954417.2 Gewicht in Gramm: 1.
Idioma: Inglés
Editorial: Springer, 2002
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Condición: New. In.
Idioma: Inglés
Editorial: Springer-Verlag New York Inc., US, 2002
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Librería: Rarewaves.com USA, London, LONDO, Reino UnidoRarewaves.com USA
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Hardback. Condición: New. 2002 ed. 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.
Idioma: Inglés
Editorial: Springer, 2002
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Librería: California Books, Miami, FL, Estados Unidos de AmericaCalifornia Books
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Condición: New.
Idioma: Inglés
Editorial: Springer-Verlag New York Inc., US, 2002
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Hardback. Condición: New. 2002 ed. 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.
Idioma: Inglés
Editorial: Springer, Humana, 2002
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Buch. Condición: 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 l…inear 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.
Idioma: Inglés
Editorial: Springer, 2002
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Librería: BUCHSERVICE / ANTIQUARIAT Lars Lutzer, Wahlstedt, AlemaniaBUCHSERVICE / ANTIQUARIAT Lars Lutzer
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Condición: gut. 2002. A Distribution-Free Theory of Nonparametric Regression (Springer Series in Statistics) In englischer Sprache. pages.
Idioma: Inglés
Editorial: Springer New York, 2002
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Librería: moluna, Greven, Alemaniamoluna
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Gebunden. Condición: 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.
Idioma: Inglés
Editorial: Springer, Springer Aug 2002, 2002
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Buch. Condición: 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.
Idioma: Inglés
Editorial: Springer, Humana Aug 2002, 2002
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. 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 KG, Sachsenplatz 4-6, 1201 Wien 672 pp. Englisch.
