Discriminant Analysis Using Least Absolute Deviations
Chau-Kwor Lee,J. Keith Ord +1 more
TLDR
In this paper, a least absolute deviations regression procedure is developed which is simpler to use and does not suffer from any lack of invariance, and a simulation study shows it to be at least as effective as any of the methods previously discussed for normal and heavy-tailed distributions.Abstract:
Several linear programming methods have been suggested as discrimination procedures. A least absolute deviations regression procedure is developed here which is simpler to use and does not suffer from any lack of invariance. A simulation study shows it to be at least as effective as any of the methods previously discussed for normal and heavy-tailed distributions.read more
Citations
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Multicriteria classification and sorting methods: A literature review
TL;DR: The objective of this paper is to review the research conducted on the framework of the multicriteria decision aiding (MCDA).
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Nontraditional approaches to statistical classification: Some perspectives on L_p-norm methods
TL;DR: Proposals to integrate a number of the most important Lp-norm methods proposed to date within a unified framework are made, emphasizing their conceptual differences and similarities, rather than focusing on mathematical detail.
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Multicriteria Preference Disaggregation for Classification Problems with an Application to Global Investing Risk
TL;DR: Among the six evaluation models developed, one (MHDIS) classifies correctly all countries into the appropriate groups and outperformed the other five methods in a 10-fold validation experiment, promising for the study of emerging new markets in fast-growing regions.
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Combining discriminant methods in solving classification problems in two-group discriminant analysis
Kim Fung Lam,Jane W. Moy +1 more
TL;DR: A method is presented that combines several discriminant methods to predict the classification of new observations and Simulation experiments are run to test this combining technique.
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Minimizing deviations from the group mean: a new linear programming approach for the two-group classification problem
TL;DR: A new linear programming approach to solve the two-group classification problem in discriminant analysis that has an advantage of obtaining more stable classification function across different samples than most of the existing linear programming approaches.
References
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Optimal Estimation of Executive Compensation by Linear Programming
TL;DR: It will be shown how the methods of linear programming may be used to obtain estimates of parameters when more usual methods, such as “least squares,” are difficult or impossible to apply.
Journal ArticleDOI
Pitfalls in the application of discriminant analysis in business, finance, and economics
TL;DR: The purpose of this paper is to discuss problems of application of discriminant analysis techniques and the prospects for statistical research on the application of the techniques.
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Asymptotic Theory of Least Absolute Error Regression
Gilbert W. Bassett,Roger Koenker +1 more
TL;DR: In this paper, the estimator which minimizes the sum of absolute residuals is demonstrated to be consistent and asymptotically Gaussian with covariance matrix ω2 Q -1.
Journal ArticleDOI
Simple but powerful goal programming models for discriminant problems
Ned Freed,Fred Glover +1 more
TL;DR: In this paper, the authors suggest alternative assignment procedures, utilizing a set of interrelated goal programming formulations, and demonstrate the potential of these procedures to play a significant part in addressing the discriminant problem, and indicate fundamental ideas that lay the foundation for other sophisticated approaches.
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