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A robust methodology for discriminant analysis based on least‐absolute‐value estimation
Author(s) -
Glorfeld Louis W.
Publication year - 1990
Publication title -
managerial and decision economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.288
H-Index - 51
eISSN - 1099-1468
pISSN - 0143-6570
DOI - 10.1002/mde.4090110408
Subject(s) - linear discriminant analysis , outlier , optimal discriminant analysis , discriminant , multiple discriminant analysis , identification (biology) , computer science , statistics , pattern recognition (psychology) , kernel fisher discriminant analysis , artificial intelligence , data mining , mathematics , botany , facial recognition system , biology
Many important real‐world discriminant problems require application of linear discriminant analysis to data that are contaminated with outliers and do not approximate the assumptions required for the near‐optimal performance of the standard statistical model. A robust diagnostic methodology is developed based on least‐absolute‐value estimation which addresses both descriptive and classification objectives of discriminant analysis. When used in conjunction with standard discriminant analysis this methodology allows the assessment of the impact of contamination on standard discriminant analysis and identification of possible assumption violations and contaminating data points through an informal graphical display. An application to the mortgage loan‐granting problem is used as a basis for demonstrating the methodology.