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Multivariate nonparametric regressio...

Klemelä, Jussi, (1965-)

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 Klemela.
Reminder of title:	with R and applications to finance /
Author:	Klemelä, Jussi,
Published:	Hoboken, New Jersey :Wiley,c2014.
Description:	xxiii, 367 p. :ill. ;24 cm.
Subject:	FinanceMathematical models.
ISBN:	9780470384428 (hbk.) :

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 Klemela. - Hoboken, New Jersey :Wiley,c2014. - xxiii, 367 p. :ill. ;24 cm. - Wiley series in computational statistics ;699.

Includes bibliographical references and index.

"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: 9780470384428 (hbk.) :NT$3138

LCCN: 2013042095Subjects--Topical Terms:

183782
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 /
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008 151012s2014 njua b 001 0 eng
010 $a 2013042095
020 $a 9780470384428 (hbk.) : $c NT$3138
035 $a 17922385
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 709811
245 1 0 $a Multivariate nonparametric regression and visualization : $b with R and applications to finance / $c Jussi Klemela.
260 $a Hoboken, New Jersey : $b Wiley, $c c2014.
300 $a xxiii, 367 p. : $b ill. ; $c 24 cm.
490 0 $a Wiley series in computational statistics ; $v 699
504 $a Includes bibliographical references and index.
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 183782
650 0 $a Regression analysis. $3 181872
650 0 $a Visualization. $3 182994