Technical Note—Dynamic Data-Driven Estimation of Nonparametric Choice Models

Nam Ho-Nguyen () and Fatma Kılınç-Karzan ()
Additional contact information
Nam Ho-Nguyen: Discipline of Business Analytics, The University of Sydney, New South Wales 2006, Australia
Fatma Kılınç-Karzan: Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213

Operations Research, 2021, vol. 69, issue 4, 1228-1239

Abstract: We study nonparametric estimation of choice models, which was introduced to alleviate unreasonable assumptions in traditional parametric models and is prevalent in several application areas. Existing literature focuses only on the static observational setting where all of the observations are given up front and lacks algorithms that provide explicit convergence rate guarantees or an a priori analysis for the model accuracy versus sparsity trade-off on the actual estimated model returned. As opposed to this, we focus on estimating a nonparametric choice model from observational data in a dynamic setting, where observations are obtained over time. We show that choice model estimation can be cast as a convex-concave saddle point joint estimation and optimization problem, and we provide an online convex optimization-based primal-dual framework for deriving algorithms to solve this problem. By tailoring our framework carefully to the choice model estimation problem, we obtain tractable algorithms with provable convergence guarantees and explicit bounds on the sparsity of the estimated model. Our numerical experiments confirm the effectiveness of the algorithms derived from our framework.

Keywords: marketing: choice models; programming: nonlinear: algorithms; Revenue Management and Market Analytics; nonparametric choice; first-order methods; dynamic data (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/opre.2020.2077 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:69:y:2021:i:4:p:1228-1239

Access Statistics for this article

More articles in Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().