• Journal of the Korean Operations Research and Management Science Society
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• v.27 no.2
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• pp.1-13
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• 2002

In this paper, we compares the business failure prediction accuracy among Linear Programming Discriminant Analysis(LPDA) model, Multivariate Discriminant Analysis (MDA) model and logit analysis model. The Data for 417 companies analyzed were gathered from KIS-FAS Published by Korea Information Service in 1999. The result of comparison for four time horizons shows that LPDA Is advantageous in prediction accuracy over the other two models when over all tilt ratio and business failure accuracy are considered simultaneously.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.447-456
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• 2007

Recently, the multivariate statistical analysis has been used to analyze meaningful information for various data. In this paper, we develope the multivariate statistical analysis system combined with Fisher discriminant analysis, logistic regression, neural network, and decision tree using visual basic 6.0.

• Korean journal of applied entomology
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• v.32 no.1
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• pp.30-41
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• 1993

In the present investigation, some multivariate discriminant analyses were done for each population of the native honeybee workers (Apis cerana), which were selected for 15 different localities in spring and 16 in summer form Korea. when the comparison of both seasons for different populations and regardless of seasons were conducted, all the classification results revealed that the differences were significantly prominent. And the length of fore tibia(FTL) was the best contributed character among the 47 morphometric characters used in the analysis.

• Journal of the Korean Statistical Society
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• v.30 no.1
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• pp.139-151
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• 2001

Although lots of works are done in influence diagnostics, results in the multivariate analysis are quite rare. One of recent works done by Fung(1995) is about the single case influence diagnostics in the linear discriminant analysis. In this paper we extend Fung's results to the multiple cases diagnostics which are necessary in the linear discriminant analysis for two reasons among others; First, the masking effect cannot be detected by single case diagnostics and secondly two populations are concerned in the discriminant analysis, i.e., influential cases can occur in one or both populations.

• Communications for Statistical Applications and Methods
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• v.28 no.1
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• pp.89-97
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• 2021

Due to boundedness and sum constraint, compositional data are often transformed by logratio transformation and their transformed data are put into traditional binary classification or discriminant analysis. However, it may be problematic to directly apply traditional multivariate approaches to the transformed data because class distributions are not Gaussian and Bayes decision boundary are not polynomial on the transformed space. In this study, we propose to use flexible classification approaches to transformed data for compositional data classification. Empirical studies using synthetic and real examples demonstrate that flexible approaches outperform traditional multivariate classification or discriminant analysis.

• Communications for Statistical Applications and Methods
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• v.4 no.1
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• pp.49-63
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• 1997

We consider the problem of classifying a p x 1 observation into one of two multivariate normal populations when the training smaples contain a block of missing observation. A new classification procedure is proposed which is a linear combination of two discriminant functions, one based on the complete samples and the other on the incomplete samples. The new discriminant function is easy to use.

• The Korean Journal of Applied Statistics
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• v.5 no.1
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• pp.103-111
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• 1992

The projection pursuit method has been gussested as a technique for the analysis of the multivariate data. This method seeks out interesting linear projections of the multivariate data onto a line of a plane to solve the curse or dimensionality. In this paper we developed the discriminant analysis by using the projection method and simulations were used for comparison between this and other existing discriminant analysis methods.

• Korean Journal of Pharmacognosy
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• v.44 no.4
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• pp.344-349
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• 2013

Near infrared spectroscopy (NIRS) combined with multivariate analysis was used to discriminate the geographical origin of Alisma orientale from Korea (n=94) and China (n=72). Two-thirds of samples were selected randomly for the training set, and one-third of samples for the test set. Second derivative was used for the pretreatment of NIR spectra. Partial least square discriminant analysis (PLS-DA) models correctly discriminated 100% of the Korean and Chinese A. orientale samples. These results demonstrate the potential use of NIR spectroscopy combined with multivariate analysis as a rapid and accurate method to discriminate A. orientale according to their geographical origin.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.10a
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• pp.31-34
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• 1996

The extensive researches have compared the performance of neural networks(NN) with those of various statistical techniques for the classification problem. The empirical results of these comparative studies have indicated that the neural networks often outperform the traditional statistical techniques. Moreover, there are some efforts that try to combine various classification methods, especially multivariate discriminant analysis with neural networks. While these efforts improve the performance, there exists a problem violating robust assumptions of multivariate discriminant analysis that are multivariate normality of the independent variables and equality of variance-covariance matrices in each of the groups. On the contrary, cluster analysis alleviates this assumption like neural networks. We propose a new approach to classification problems by combining the cluster analysis with neural networks. The resulting predictions of the composite model are more accurate than each individual technique.

• Korean journal of applied entomology
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• v.32 no.1
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• pp.42-50
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• 1993

Some multivariate discriminant analyses were done for each population of the native honeybee workers (Apis cerana), which were selected for 11 different localities in spring and 12 in summer from Korea. When the comparison for different localities was conducted, the correct assignment was averaged at 91.67% in spring and 88.44% in summer. And for the comparison between the 2 different seasons, it was averaged at 97.58%. Whereas, that regardless of seasons revealed the lowest correct assignment at 70.16%.