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Robust linear discriminant models to solve financial crisis in banking sectors

Lim, Yai-Fung and Syed Yahaya, Sharipah Soaad and Idris, Faoziah and Ali, Hazlina and Omar, Zurni (2014) Robust linear discriminant models to solve financial crisis in banking sectors. In: 3rd International Conference on Quantitative Sciences and its Applications (ICOQSIA 2014), 12–14 August 2014, Langkawi, Kedah Malaysia.

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Linear discriminant analysis (LDA) is a widely-used technique in patterns classification via an equation which will minimize the probability of misclassifying cases into their respective categories.However, the performance of classical estimators in LDA highly depends on the assumptions of normality and homoscedasticity. Several robust estimators in LDA such as Minimum Covariance Determinant (MCD), S-estimators and Minimum Volume Ellipsoid (MVE) are addressed by many authors to alleviate the problem of non-robustness of the classical estimates. In this paper, we investigate on the financial crisis of the Malaysian banking institutions using robust LDA and classical LDA methods. Our objective is to distinguish the "distress" and "non-distress" banks in Malaysia by using the LDA models. Hit ratio is used to validate the accuracy predictive of LDA models. The performance of LDA is evaluated by estimating the misclassification rate via apparent error rate. The results and comparisons show that the robust estimators provide a better performance than the classical estimators for LDA

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: financial crisis; hit ratio; Linear discriminant analysis; non-normality; robust estimators
Subjects: H Social Sciences > HG Finance
Divisions: School of Quantitative Sciences
Depositing User: Prof. Madya Dr. Sharipah Soaad Syed Yahaya
Date Deposited: 20 Dec 2015 08:01
Last Modified: 19 May 2016 02:59

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