Appendix V : Preference Mapping from JAR Data Using Tree-Based Regressions / Jean-Francois Meullenet, Rui Xiong
- Author
- Xiong, Rui
- Physical Description
- 1 online resource (4 pages) : illustrations, figures, tables
- Additional Creators
- Meullenet, Jean-Francois, American Society for Testing and Materials, and ASTM International
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License restrictions may limit access. - Summary
- Penalty analysis offers a method to consider the individual effects of JAR ratings on Overall Liking (OAL), but does not provide a way to assess the impacts of simultaneous changes in JAR ratings on Overall Liking. Standard multiple regression is of limited use in this situation because of its strong assumptions of linearity. A form of non-parametric regression, which we will refer to as tree-based regression, removes that assumption and allows you to determine the combinations of the JAR ratings that have the strongest impact on Overall Liking. There are wide variety of tree-based regressions packages available, such as CART, MARS, KnowledgeSeeger, and SPSS AnswerTree, as well as free implementations such as part in R. This example will use MARS (multivariate adaptive regression splines) as its example [1]. This is commercial software, sold by Salford Systems (http://www.salfordsystems.com/) [2].
- Dates of Publication and/or Sequential Designation
- Volume 2009, Issue 63 (January 2009)
- Subject(s)
- ISBN
- 9780803167391 (e-ISBN)
9780803170100
0803170106 - Digital File Characteristics
- text file PDF
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- "ASTM Stock Number: MNL11503M".
- Bibliography Note
- Includes bibliographical references 2.
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- Also available online via the World Wide Web. Tables of contents and abstracts freely available; full-text articles available by subscription.
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- Electronic reproduction. W. Conshohocken, Pa. : ASTM International, 2009. Mode of access: World Wide Web. System requirements: Web browser. Access may be restricted to users at subscribing institutions.
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- ASTM International PDF Purchase price USD25.
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