A Distribution-Free Theory of Nonparametric Regression:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer New York
2002
|
| Schriftenreihe: | Springer Series in Statistics
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| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | 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 |
| Beschreibung: | 1 Online-Ressource (XVI, 650 p) |
| ISBN: | 9780387224428 9780387954417 |
| ISSN: | 0172-7397 |
| DOI: | 10.1007/b97848 |
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Datensatz im Suchindex
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| discipline | Mathematik |
| doi_str_mv | 10.1007/b97848 |
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| isbn | 9780387224428 9780387954417 |
| issn | 0172-7397 |
| language | English |
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| spelling | Györfi, László Verfasser aut A Distribution-Free Theory of Nonparametric Regression by László Györfi, Michael Kohler, Adam Krzyżak, Harro Walk New York, NY Springer New York 2002 1 Online-Ressource (XVI, 650 p) txt rdacontent c rdamedia cr rdacarrier Springer Series in Statistics 0172-7397 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 Statistics Mathematical statistics Statistical Theory and Methods Statistik Nichtparametrische Statistik (DE-588)4226777-8 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 gnd rswk-swf Regressionsanalyse (DE-588)4129903-6 s Nichtparametrische Statistik (DE-588)4226777-8 s 1\p DE-604 Kohler, Michael Sonstige oth Krzyżak, Adam Sonstige oth Walk, Harro Sonstige oth https://doi.org/10.1007/b97848 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Györfi, László A Distribution-Free Theory of Nonparametric Regression Statistics Mathematical statistics Statistical Theory and Methods Statistik Nichtparametrische Statistik (DE-588)4226777-8 gnd Regressionsanalyse (DE-588)4129903-6 gnd |
| subject_GND | (DE-588)4226777-8 (DE-588)4129903-6 |
| title | A Distribution-Free Theory of Nonparametric Regression |
| title_auth | A Distribution-Free Theory of Nonparametric Regression |
| title_exact_search | A Distribution-Free Theory of Nonparametric Regression |
| title_full | A Distribution-Free Theory of Nonparametric Regression by László Györfi, Michael Kohler, Adam Krzyżak, Harro Walk |
| title_fullStr | A Distribution-Free Theory of Nonparametric Regression by László Györfi, Michael Kohler, Adam Krzyżak, Harro Walk |
| title_full_unstemmed | A Distribution-Free Theory of Nonparametric Regression by László Györfi, Michael Kohler, Adam Krzyżak, Harro Walk |
| title_short | A Distribution-Free Theory of Nonparametric Regression |
| title_sort | a distribution free theory of nonparametric regression |
| topic | Statistics Mathematical statistics Statistical Theory and Methods Statistik Nichtparametrische Statistik (DE-588)4226777-8 gnd Regressionsanalyse (DE-588)4129903-6 gnd |
| topic_facet | Statistics Mathematical statistics Statistical Theory and Methods Statistik Nichtparametrische Statistik Regressionsanalyse |
| url | https://doi.org/10.1007/b97848 |
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