Abstract
The Mahalanobis-Taguchi system (MTS) is a diagnosis and forecasting method using multivariate data. Mahalanobis distance (MD) is a measure based on correlations between the variables and patterns that can be identified and analyzed with respect to a base or reference group. The MTS is of interest because of its reported accuracy in forecasting using small, correlated data sets. This is the type of data that is encountered with consumer vehicle ratings. MTS enables a reduction in dimensionality and the ability to develop a scale based on MD values. MTS identifies a set of useful variables from the complete data set with equivalent correlation and considerably less time and data. This article presents the application of the MTS, its applicability in identifying a reduced set of useful variables in multidimensional systems, and a comparison of results with those obtained from a standard statistical approach to the problem.
Recommended Citation
K. Paryani et al., "Applying the Mahalanobis-Taguchi System to Vehicle Handling," Concurrent Engineering Research and Applications, SAGE Publications, Jan 2006.
The definitive version is available at https://doi.org/10.1177/1063293X06073568
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Mahalanobis Taguchi; Mahalanobis Distance; Multivariate; Orthogonal Array; Orthogonalization; Pattern Recognition; Signal-To-Noise Ratio
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2006 SAGE Publications, All rights reserved.
Publication Date
01 Jan 2006
