MULTIVARIATE NORMALITY AND A BOND RATING DECISION MODEL*
验证了债券评级决策模型中多元正态性假设的合理性，通过对市政债券样本数据进行单变量和多元正态性检验，并应用正态化变换，使数据近似满足多元正态分布，从而支持基于判别分析的决策模型。
ABSTRACT An assumption of multivariate normality for a decision model is validated in this paper. Measurements for the independent variables of a bond rating model were taken from a sample of municipal bonds. Three methods for examining both univariate and multivariate normality (including normal probability plots) are described and applied to the bond data. The results imply, after applying normalizing transformations to four of the variables, that the data reasonably approximate multivariate normality, thereby validating a distributional requirement of the discriminant‐analysis‐based decision model. The methods described in the paper may also be used by others interested in examining multivariate normality assumptions of decision models.