A GENERALIZABLE FORMULATION OF CONDITIONAL LOGIT WITH DIAGNOSTICS

• 成果类型: Article
• 署名作者: SOOFI, ES
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2290219
• 发表日期: 1992
• 页码: 812-816
• 关键词: maximum-entropy; INFORMATION

摘要

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.