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Multivariate nonparametric regressio...

Klemelä, Jussi, (1965-)

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Multivariate nonparametric regression and visualization : = with R and applications to finance /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Multivariate nonparametric regression and visualization :/ Jussi Klemelä.
Reminder of title:	with R and applications to finance /
Author:	Klemelä, Jussi,
Published:	Hoboken, NJ :Wiley, : c2014.,
Description:	xxiii, 367 p. :ill. (some col.) ;24 cm.
Subject:	Finance - Mathematical models. -
ISBN:	9780470384428

Multivariate nonparametric regression and visualization : = with R and applications to finance /
Klemelä, Jussi,1965-

Multivariate nonparametric regression and visualization :with R and applications to finance /Jussi Klemelä. - Hoboken, NJ :Wiley,c2014. - xxiii, 367 p. :ill. (some col.) ;24 cm. - Wiley series in computational statistics.

Includes bibliographical references (p. 351-359) and indexes.

"This book uniquely utilizes visualization tools to explain and study statistical learning methods. Covering classification and regression, the book is divided into two parts. First, various visualization methods are introduced and explained. Here, the reader is presented with applications of visualization techniques to learning samples (including projection pursuit, graphical matrices, and parallel coordinate plots) as well as functions, and sets. Next, the author provides a "toolbox" that contains formal definitions of the methods applied in the book and then proceeds to present visualizations of classified learning samples and classified test samples. Visualization methods are applied for the initial exploration of data, to identify the correct type of classifier, and to estimate the best achievable classification error. Once identified, the classifier's properties, proper uses, and overall performance are demonstrated and measured using visualization methods. Key areas of coverage include linear methods, kernel methods, additive models and trees, boosting, support vector machines, and nearest neighbor methods In addition to providing applications to engineering and biomedicine, the author also uses financial data sets as real data examples to illustrate nonparametric function estimation. The author's own R software is used throughout to reproduce and modify the book's computations and research. Readers can duplicate these applications using the software, available via the book's related Web site"--

ISBN: 9780470384428US114.95

LCCN: 2013042095Subjects--Topical Terms:

578740
Finance
--Mathematical models.

LC Class. No.: HG176.5 / .K55 2014

Dewey Class. No.: 519.5/36

Multivariate nonparametric regression and visualization : = with R and applications to finance /
LDR:02408cam a2200253 a 4500 001 2017627
003 DLC
005 20150825154540.0
008 161003s2014 njua b 001 0 eng
010 $a 2013042095
020 $a 9780470384428 $q (hardback) : $c US114.95
020 $a 0470384425 $q (hardback)
020 $z 9781118593509 $q (epub)
020 $z 9781118838044 $q (pdf)
020 $z 9781118593585 $q (mobi)
040 $a DLC $b eng $c DLC $d DLC
042 $a pcc
050 0 0 $a HG176.5 $b .K55 2014
082 0 0 $a 519.5/36 $2 23
100 1 $a Klemelä, Jussi, $d 1965- $3 2184953
245 1 0 $a Multivariate nonparametric regression and visualization : $b with R and applications to finance / $c Jussi Klemelä.
260 $a Hoboken, NJ : $b Wiley, $c c2014.
300 $a xxiii, 367 p. : $b ill. (some col.) ; $c 24 cm.
490 0 $a Wiley series in computational statistics
504 $a Includes bibliographical references (p. 351-359) and indexes.
520 $a "This book uniquely utilizes visualization tools to explain and study statistical learning methods. Covering classification and regression, the book is divided into two parts. First, various visualization methods are introduced and explained. Here, the reader is presented with applications of visualization techniques to learning samples (including projection pursuit, graphical matrices, and parallel coordinate plots) as well as functions, and sets. Next, the author provides a "toolbox" that contains formal definitions of the methods applied in the book and then proceeds to present visualizations of classified learning samples and classified test samples. Visualization methods are applied for the initial exploration of data, to identify the correct type of classifier, and to estimate the best achievable classification error. Once identified, the classifier's properties, proper uses, and overall performance are demonstrated and measured using visualization methods. Key areas of coverage include linear methods, kernel methods, additive models and trees, boosting, support vector machines, and nearest neighbor methods In addition to providing applications to engineering and biomedicine, the author also uses financial data sets as real data examples to illustrate nonparametric function estimation. The author's own R software is used throughout to reproduce and modify the book's computations and research. Readers can duplicate these applications using the software, available via the book's related Web site"-- $c Provided by publisher.
650 0 $a Finance $x Mathematical models. $3 578740
650 0 $a Visualization. $3 586179
650 0 $a Regression analysis. $3 529831