DATA ANALYSIS FOR SIMULATION EXPERIMENTS: APPLICATION OF A DISTRIBUTION‐FREE MULTIPLE COMPARISONS PROCEDURE
针对仿真数据不满足正态性和方差齐性假设的问题，提出一种基于Friedman秩和检验的无分布方法，适用于使用公共随机数流进行分块比较的竞争方案分析。
Abstract The analysis of simulation data in the evaluation of competing alternatives presents a problem for the analyst using conventional statistical techniques. The assumptions of normality and common variances generally cause difficulties since many classes of simulation experiments typically violate both of these assumptions. In addition, the analyst is usually interested in comparing competing alternatives using environments that are as close to identical as possible. In these situations, blocking is desirable and can usually be accomplished by using common random number streams. This paper discusses a distribution‐free method based on Friedman's rank sums test that can be used to analyze simulation results exhibiting the above characteristics. The procedure requires only independence within treatments.