Logistic Regression and Discriminant Analysis by Ordinary Least Squares
证明，当数据满足正态假设时，多项逻辑回归的最大似然估计可通过普通最小二乘法计算，并指出判别函数估计在非正态情况下也值得推广，尤其适用于大数据探索。
If the observations for fitting a polytomous logistic regression model satisfy certain normality assumptions, the maximum likelihood estimates of the regression coefficients are the discriminant function estimates. This article shows that these estimates, their unbiased counterparts, and associated test statistics for variable selection can be calculated using ordinary least squares regression techniques, thereby providing a convenient method for fitting logistic regression models in the normal case. Evidence is given indicating that the discriminant function estimates and test statistics merit wider use in nonnormal cases, especially in exploratory work on large data sets.