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The Elements of Statistical Learning

The Elements of Statistical Learning BOOK REVIEWS 267 Chapter 11 inclu des more case studies in oth er ar eas, ranging from man- Chapter 1, “ Int roduction,” gi ves some of th e e xamples that th e a uthors u se ufacturing to marke ting rese arch. Chapter 1 2 c oncludes the bo ok with so me as the mo tivation fo r rea ding the b ook. Ch apter 2 , “ Ove rview of Su pervised commentary about th e scie nti c contributions of MTS . Learning,” is the b ackground fo r a ll of th e re st of th e bo ok e xcept th e nal c hap- ter. U nfortunately, it contain s no e xamples, only ma thematical statements about The T aguchi me thod fo r d esign of e xperiment h as generated c onsider- different prediction meth ods, decision t heory, va rious lo cal estimation metho ds, able controversy in the sta tistical community ove r the past fe w decades. The function app roximation, and restricte d estimators. For statisticia ns, th is is im- MTS/MTGS me thod se ems to lea d another sou rce http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Technometrics Taylor & Francis

The Elements of Statistical Learning

Ziegel, Eric R
Technometrics , Volume 45 (3): 2 – Aug 1, 2003
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References (7)

Publisher
Taylor & Francis
Copyright
© American Statistical Association and the American Society for Quality
ISSN
1537-2723
eISSN
0040-1706
DOI
10.1198/tech.2003.s770
Publisher site
See Article on Publisher Site

Abstract

BOOK REVIEWS 267 Chapter 11 inclu des more case studies in oth er ar eas, ranging from man- Chapter 1, “ Int roduction,” gi ves some of th e e xamples that th e a uthors u se ufacturing to marke ting rese arch. Chapter 1 2 c oncludes the bo ok with so me as the mo tivation fo r rea ding the b ook. Ch apter 2 , “ Ove rview of Su pervised commentary about th e scie nti c contributions of MTS . Learning,” is the b ackground fo r a ll of th e re st of th e bo ok e xcept th e nal c hap- ter. U nfortunately, it contain s no e xamples, only ma thematical statements about The T aguchi me thod fo r d esign of e xperiment h as generated c onsider- different prediction meth ods, decision t heory, va rious lo cal estimation metho ds, able controversy in the sta tistical community ove r the past fe w decades. The function app roximation, and restricte d estimators. For statisticia ns, th is is im- MTS/MTGS me thod se ems to lea d another sou rce

Journal

Technometrics – Taylor & Francis

Published: Aug 1, 2003

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