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Inhaltsangabe
This book surveys the uses of smoothing methods in statistics. The coverage has an applied focus, and is very broad, including simple and complex univariate and multivariate density estimation, nonparametric regression estimation, categorical data smoothing, and applications of smoothing to other areas of statistics.
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Críticas
"...an excellent reference to the field and has no rival in terms of accessibility, coverage, and utility." (Jnl. of the Am. Statistical Association)
"...an excellent overview of smoothing methods and concepts, presenting material in an intuitive manner with many interesting graphics." (Technometrics)
Reseña del editor
Focussing on applications, this book covers a very broad range, including simple and complex univariate and multivariate density estimation, nonparametric regression estimation, categorical data smoothing, and applications of smoothing to other areas of statistics. It will thus be of particular interest to data analysts, as arguments generally proceed from actual data rather than statistical theory, while the "Background Material" sections will interest statisticians studying the field. Over 750 references allow researchers to find the original sources for more details, and the "Computational Issues" sections provide sources for statistical software that use the methods discussed. Each chapter includes exercises with a heavily computational focus based upon the data sets used in the book, making it equally suitable as a textbook for a course in smoothing.
„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.
- VerlagSpringer
- Erscheinungsdatum2011
- ISBN 10 1461284724
- ISBN 13 9781461284727
- EinbandTapa blanda
- SpracheEnglisch
- Anzahl der Seiten356
- Kontakt zum HerstellerNicht verfügbar
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Smoothing Methods in Statistics
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Focussing on applications, this book covers a very broad range, including simple and complex univariate and multivariate density estimation, nonparametric regression estimation, categorical data smoothing, and applications of smoothing to other areas of sta. Bestandsnummer des Verkäufers 4191042
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Smoothing Methods in Statistics (Springer Series in Statistics)
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Smoothing Methods in Statistics
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The existence of high speed, inexpensive computing has made it easy to look at data in ways that were once impossible. Where once a data analyst was forced to make restrictive assumptions before beginning, the power of the computer now allows great freedom in deciding where an analysis should go. One area that has benefited greatly from this new freedom is that of non parametric density, distribution, and regression function estimation, or what are generally called smoothing methods. Most people are familiar with some smoothing methods (such as the histogram) but are unlikely to know about more recent developments that could be useful to them. If a group of experts on statistical smoothing methods are put in a room, two things are likely to happen. First, they will agree that data analysts seriously underappreciate smoothing methods. Smoothing meth ods use computing power to give analysts the ability to highlight unusual structure very effectively, by taking advantage of people's abilities to draw conclusions from well-designed graphics. Data analysts should take advan tage of this, they will argue. Bestandsnummer des Verkäufers 9781461284727
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Smoothing Methods in Statistics
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Springer-Verlag New York Inc., 2011
ISBN 10: 1461284724
ISBN 13: 9781461284727
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Smoothing Methods in Statistics
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Focussing on applications, this book covers a very broad range, including simple and complex univariate and multivariate density estimation, nonparametric regression estimation, categorical data smoothing, and applications of smoothing to other areas of statistics. It will thus be of particular interest to data analysts, as arguments generally proceed from actual data rather than statistical theory, while the 'Background Material' sections will interest statisticians studying the field. Over 750 references allow researchers to find the original sources for more details, and the 'Computational Issues' sections provide sources for statistical software that use the methods discussed. Each chapter includes exercises with a heavily computational focus based upon the data sets used in the book, making it equally suitable as a textbook for a course in smoothing. 356 pp. Englisch. Bestandsnummer des Verkäufers 9781461284727
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Smoothing Methods in Statistics
Verlag:
Springer New York, Springer US Sep 2011, 2011
ISBN 10: 1461284724
ISBN 13: 9781461284727
Neu
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Taschenbuch. Zustand: Neu. Neuware -The existence of high speed, inexpensive computing has made it easy to look at data in ways that were once impossible. Where once a data analyst was forced to make restrictive assumptions before beginning, the power of the computer now allows great freedom in deciding where an analysis should go. One area that has benefited greatly from this new freedom is that of non parametric density, distribution, and regression function estimation, or what are generally called smoothing methods. Most people are familiar with some smoothing methods (such as the histogram) but are unlikely to know about more recent developments that could be useful to them. If a group of experts on statistical smoothing methods are put in a room, two things are likely to happen. First, they will agree that data analysts seriously underappreciate smoothing methods. Smoothing meth ods use computing power to give analysts the ability to highlight unusual structure very effectively, by taking advantage of people's abilities to draw conclusions from well-designed graphics. Data analysts should take advan tage of this, they will argue.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 356 pp. Englisch. Bestandsnummer des Verkäufers 9781461284727
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