Random Projection Estimation of Discrete-Choice Models with Large Choice Sets
Jan 1, 2019·,·
1 min read
Khai Chiong
Matt Shum
Abstract
This paper introduces random projection methods for the estimation of discrete-choice models with large choice sets. By projecting high-dimensional choice features into lower dimensions, the authors achieve efficient estimation without compromising accuracy.
Type
Publication
Management Science, Volume 65, No. 1, pages 256–271, January 2019
Chiong and Shum tackle the computational challenges posed by discrete-choice models with large choice sets. Their random projection estimator dramatically reduces dimensionality, allowing researchers to analyze complex choice data efficiently. The approach is particularly useful for modern marketing and transportation applications involving many alternatives.