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A Generalizable Formulation of Conditional Logit With Diagnostics
Ehsan S. Soofi
Journal of the American Statistical Association
Vol. 87, No. 419 (Sep., 1992), pp. 812-816
Stable URL: http://www.jstor.org/stable/2290219
Page Count: 5
You can always find the topics here!Topics: Entropy, Probabilities, Statistical models, Statistics, Linear regression, Modeling, Conditional probabilities, Maximum likelihood estimation, Normalizing, Statistical estimation
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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.
Journal of the American Statistical Association © 1992 American Statistical Association