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| Altman Z-Score: 기업 파산 예측× | 선형 판별 분석 (LDA)× | |
|---|---|---|
| 분야≠ | 재무학 | 머신러닝 |
| 계열≠ | Regression model | Latent structure |
| 기원 연도≠ | 1968 | 1936 |
| 창시자≠ | Edward Altman | Fisher, R. A. |
| 유형≠ | Multiple discriminant analysis scoring model | Supervised dimensionality reduction and linear classifier |
| 원전≠ | Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. DOI ↗ | Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188. DOI ↗ |
| 별칭≠ | Altman's Z-Score Model, Multiple Discriminant Analysis Bankruptcy Model, Z-Score Financial Distress Model, Altman Z-Skoru | LDA, Fisher's discriminant analysis, Fisher linear discriminant, normal discriminant analysis |
| 관련≠ | 3 | 4 |
| 요약≠ | The Altman Z-Score is a linear discriminant model developed by Edward I. Altman in 1968 to predict corporate bankruptcy using five accounting-based financial ratios. Derived through multiple discriminant analysis on a matched sample of 66 US manufacturing firms, the model combines liquidity, profitability, leverage, solvency, and activity ratios into a single composite score that classifies firms as financially sound, distressed, or in a grey zone. | Linear Discriminant Analysis is a supervised method for dimensionality reduction and classification, introduced by Ronald A. Fisher in 1936, that finds linear combinations of features which maximally separate predefined classes while preserving as much class-discriminatory information as possible. It simultaneously serves as a feature-projection technique and a probabilistic classifier, making it one of the foundational methods in pattern recognition and statistical learning. |
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