Springer book chapter on “Nonparametric Estimation of Choice Models”

Demand forecasting is the first step before making any decisions, and forecasting using choice-based demand models has become the de-facto approach for operations, revenue management, and marketing applications. As more data has become available, we have been able to fit more complex (nonparametric) models, and the past decade has seen an explosion of research on this topic, particularly, in the operations management field.

I am pleased to share our recently published Springer book chapter (w/ my amazing co-author and former PhD student, Ashwin Venkataraman) reviewing the most important work in the area of nonparametric estimation of large scale choice models. These methods are designed to scale to 100s or even 1000s of products, and can efficiently estimate complex models.

If you are interested in getting a quick understanding of this topic, check out: https://srikanth-jagabathula.com/docs/nonparam_est_choice.pdf