Stepwise Location Model Choice in Mixed-Variable Discrimination
针对离散和连续变量混合的判别方法中离散变量过多的问题，提出一种向后消除法来选择离散变量，以简化位置模型并降低误差率估计的难度。
One practical drawback to the use of discrimination methods based on the location model for mixtures of discrete and continuous variables is that the smoothing techniques employed, and the subsequent estimation of error rates, limit fairly severely the allowable number of discrete variables. A backward elimination method of discrete variable selection is outlined in this paper. This can be used to identify a suitable, reduced location model for discriminant applications when the number of discrete variables is too large for direct use. It can also be used more traditionally as a variable selection procedure in discriminant analysis. Some examples are given.