NDLI: Logcontrast PLS discriminant model of compositional data

Content Provider	IEEE Xplore Digital Library
Author	Meng Jie
Copyright Year	2009
Description	Author affiliation: School of Statistics, Central University of Finance and Economics, Beijing 100081, China (Meng Jie)
Abstract	This paper studies discriminant modeling method of compositional data. The logcontrast PLS discriminant model of compositional data is proposed by adopting centered logratio transformation of compositional data and then implementing partial least squares (PLS) discriminant method to the transformed data. The model presents the following advantages: i) the transformed variable is symmetrical to the components of the original compositional data, which is favorable in explaining the modeling results; ii) PLS related methods, without strict statistical distribution assumption to the data, are typically adaptive to the compositional data notable for its unit sum constraint and complex distribution; iii) the modeling process and computation are straightforward; iv) conforming to the basic algebraic theories of compositional data, the obtained discriminant function is formally proved satisfying the logcontrast property. Finally, to evaluate this method, two experiments with simulated and real compositional data sets were performed respectively, which illustrate the validity and practicability of the model.
Starting Page	2179
Ending Page	2184
File Size	167146
Page Count	6
File Format	PDF
ISBN	9781424427222
DOI	10.1109/CCDC.2009.5191571
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Performance evaluation Discriminant Data analysis Statistical analysis Computational modeling Linear regression Gaussian distribution Least squares methods Partial Least Squares Statistical distributions Constraint theory Logcontrast Compositional Data Logistics
Content Type	Text
Resource Type	Article

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