Separating Predicted Randomness from Residual Behavior
提出一种新的随机选择模型拟合优度度量，即数据中能与模型一致的最大比例，通过将数据分为模型预测部分和残差行为部分来理解数据，并应用于四种知名模型。
Abstract We propose a novel measure of goodness of fit for stochastic choice models, that is, the maximal fraction of data that can be reconciled with the model. The procedure is to separate the data into two parts: one generated by the best specification of the model and another representing residual behavior. We claim that the three elements involved in a separation are instrumental in understanding the data. We show how to apply our approach to any stochastic choice model and then study the case of four well-known models, each capturing a different notion of randomness. We illustrate our results with an experimental data set.