Resampling estimation of discrete choice models

Nicola Ortelli, Matthieu de Lapparent and Michel Bierlaire

Journal of choice modelling, 2024, vol. 50, issue C

Abstract: In the context of discrete choice modeling, the extraction of potential behavioral insights from large datasets is often limited by the poor scalability of maximum likelihood estimation. This paper proposes a simple and fast dataset-reduction method that is specifically designed to preserve the richness of observations originally present in a dataset, while reducing the computational complexity of the estimation process. Our approach, called LSH-DR, leverages locality-sensitive hashing to create homogeneous clusters, from which representative observations are then sampled and weighted. We demonstrate the efficacy of our approach by applying it on a real-world mode choice dataset: the obtained results show that the samples generated by LSH-DR allow for substantial savings in estimation time while preserving estimation efficiency at little cost.

Keywords: Discrete choice models; Maximum likelihood estimation; Dataset reduction; Sample size; Locality-sensitive hashing (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eejocm:v:50:y:2024:i:c:s1755534523000684

DOI: 10.1016/j.jocm.2023.100467

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