A Generalizable Formulation of Conditional Logit with Diagnostics
作者:
EhsanS. Soofi,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1992)
卷期:
Volume 87,
issue 419
页码: 812-816
ISSN:0162-1459
年代: 1992
DOI:10.1080/01621459.1992.10475283
出版商: Taylor & Francis Group
数据来源: Taylor
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.KEY WORDS: Choice models; Entropy; Kullback-Leibler discrimination information function; Relative importance.
点击下载:
PDF (501KB)
返 回