AN EXPLORATION INTO BANKRUPTCY DISCRIMINANT MODEL SENSITIVITY
探讨会计数据统计特性对破产预测模型的影响，发现线性判别模型对输入数据偏离多元正态分布很敏感。
Prediction has been a central theme in much of the accounting research and theory construction and verification over the past decade. Largely ignored in such studies has been consideration of the statistical properties of accounting measures, particularly as related to the effects of those properties on the signals from prediction models that use accounting measures as inputs. This study was designed to provide preliminary insight into the magnitude of the effects of this omission, and a bankruptcy prediction model was selected to facilitate the analysis. Results indicate that the linear discriminant model (as applied to prediction of failure) is sensitive to departures of inputdata distributions from multivariate normal.