Abstract
The Mahalanobis–Taguchi system (MTS) is a relatively new collection of methods proposed for diagnosis and forecasting using multivariate data. The primary proponent of the MTS is Genichi Taguchi, who is very well known for his controversial ideas and methods for using designed experiments. The MTS results in a Mahalanobis distance scale used to measure the level of abnormality of “abnormal” items compared to a group of “normal” items. First, it must be demonstrated that a Mahalanobis distance measure based on all available variables on the items is able to separate the abnormal items from the normal items. If this is the case, then orthogonal arrays and signal-to-noise ratios are used to select an “optimal” combination of variables for calculating the Mahalanobis distances. Optimality is defined in terms of the ability of the Mahalanobis distance scale to match a prespecified or estimated scale that measures the severity of the abnormalities. In this expository article, we review the methods of the MTS and use a case study based on medical data to illustrate them. We identify some conceptual, operational, and technical issues with the MTS that lead us to advise against its use.
| Original language | English |
|---|---|
| Pages (from-to) | 1-15 |
| Number of pages | 15 |
| Journal | Technometrics |
| Volume | 45 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2003 Feb |
| Externally published | Yes |
Bibliographical note
Funding Information:The research of W. HWo.daol, Rl. Koudelik, K.-L. Tsui, and S. B. Kim was partially supported by National Science Foundation-DMI grant 9908013. K.-L. Ti’ssworuwkaaolss partially supported by The Logistic InstituteA—PascŽicia, Singapore. The work of Z. G. Stoumbos was funded in part by the Law School Admission Council (LCaSd)nbAya 2001 Rutgers Faculty of Management Research Fellowship. The opinions and conclusions contained in this publication are those of the authors and do not necessarily re ect the position or policy of LC.SWeAthnaRkaehjJsuulugm and Genichi Tauchgi for providing the medical case study dataset and allowing us to distribute it. We also thank the referees and the associate editor for their helpful comments.
Keywords
- Classification analysis
- Discriminant analysis
- Medical diagnosis
- Multivariate analysis
- Pattern recognition
- Signal-to-noise ratio
- Taguchi methods
ASJC Scopus subject areas
- Statistics and Probability
- Modelling and Simulation
- Applied Mathematics
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