MM Algorithm for General Mixed Multinomial Logit Models | Zendy

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MM Algorithm for General Mixed Multinomial Logit Models

Author(s) -

James Jonathan

Publication year - 2016

Publication title -

journal of applied econometrics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.878

H-Index - 99

eISSN - 1099-1255

pISSN - 0883-7252

DOI - 10.1002/jae.2532

Subject(s) - multinomial logistic regression , mixed logit , computer science , coding (social sciences) , maximization , mathematical optimization , logit , algorithm , logistic regression , econometrics , mathematics , statistics , machine learning

Summary This paper develops a new technique for estimating mixed logit models with a simple minorization–maximization (MM) algorithm. The algorithm requires minimal coding and is easy to implement for a variety of mixed logit models. Most importantly, the algorithm has a very low cost per iteration relative to current methods, producing substantial computational savings. In addition, the method is asymptotically consistent, efficient and globally convergent. Copyright © 2016 John Wiley & Sons, Ltd.

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