تجزیه و تحلیل تفکیک حداقل مربعات جزئی برای پیش بینی ورشکستگی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|48305||2013||11 صفحه PDF||سفارش دهید||9792 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Decision Support Systems, Volume 54, Issue 3, February 2013, Pages 1245–1255
This paper uses Partial Least Square Discriminant Analysis (PLS-DA) for the prediction of the 2008 USA banking crisis. PLS regression transforms a set of correlated explanatory variables into a new set of uncorrelated variables, which is appropriate in the presence of multicollinearity. PLS-DA performs a PLS regression with a dichotomous dependent variable. The performance of this technique is compared to the performance of 8 algorithms widely used in bankruptcy prediction. In terms of accuracy, precision, F-score, Type I error and Type II error, results are similar; no algorithm outperforms the others. Behind performance, each algorithm assigns a score to each bank and classifies it as solvent or failed. These results have been analyzed by means of contingency tables, correlations, cluster analysis and reduction dimensionality techniques. PLS-DA results are very close to those obtained by Linear Discriminant Analysis and Support Vector Machine.