A Generalizable Formulation of Conditional Logit With Diagnostics
本文展示了条件Logit模型在属性值保持约束下最大化熵，建立了最大熵与最大似然估计的对应关系，提供了易计算的诊断方法，并推广到包含初始选择概率的情形。
Abstract The conditional logit model is a multinomial logit model that permits the inclusion of choice-specific attributes. This article shows that the conditional logit model will maximize entropy given a set of attribute-value preserving constraints. A correspondence between the maximum entropy (ME) and maximum likelihood (ML) estimates for logit probabilities is established. Some easily computable and useful diagnostics for logit analysis are provided, and it is shown that an evaluation of the relative importance of attributes can be made using the ME formulation. The ME formulation is also generalized to accommodate initial choice probabilities into the logit model. An example is given. KEY WORDS: Choice models; Entropy; Kullback-Leibler discrimination information function; Relative importance.