A Heterogeneous Conditional Logit Model of Choice
放松了McFadden条件Logit模型中随机效用分布同质性的假设，提出一个概率混合模型，并通过数值结果和视觉感知实验展示其优势。
Aggregate forecasts using McFadden's conditional logit model of discrete choice harbor the unrealistic implicit assumption of a random-utility distribution that is homogeneous across both alternatives and individuals. This article relaxes that assumption. A choice model is developed that describes the random-utility component as a probability-mixture model. Some numerical results illustrate that the derived model is not constrained by the independence-of-irrelevant-alternatives property. An experimental test of visual perceptions demonstrates the potential superiority of the model.