Soft independent modeling of class analogy (SIMCA by Wold) is a statistically based class modeling approach, which has numerous modifications. Two basic statistics, the score and orthogonal distances, are utilized in all known SIMCA versions. The difference between the methods is the way they interpret and apply statistics in decision rules. The four most popular SIMCA decision rules, simple (by Massart), alternative (by Wise), combined index (by Joe Qin), and data driven (by Pomerantsev), are described in detail and compared using simulated and real‐world cases. The examples originate from various fields, and what is more important for statistical analysis is that they present different statistical structures. It is shown that depending on the way the decision rule is built, different SIMCA results can be obtained.

中文翻译：

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SIMCA中流行的决策规则：严格审查

类比的软独立建模（Wold的SIMCA）是一种基于统计的类建模方法，具有许多修改。在所有已知的SIMCA版本中都使用了两个基本统计信息，即得分和正交距离。这些方法之间的区别在于它们在决策规则中解释和应用统计信息的方式。详细介绍了四种最受欢迎​​的SIMCA决策规则，分别是：简单（Massart），替代（Wise），组合索引（Joe Qin）和数据驱动（Pomerantsev），并使用模拟和现实案例进行比较。这些示例来自各个领域，对于统计分析而言更重要的是它们呈现出不同的统计结构。结果表明，根据决策规则的构建方式，可以获得不同的SIMCA结果。