Optimization Online


Dynamic Data-Driven Estimation of Non-Parametric Choice Models

Nam Ho-Nguyen (hnh***at***andrew.cmu.edu)
Fatma Kilinc-Karzan (fkilinc***at***andrew.cmu.edu)

Abstract: We study non-parametric estimation of consumer choice models. Non-parametric approaches were introduced to alleviate unreasonable assumptions and issues of suboptimal model fit/selection present in traditional parametric approaches, and are prevalent in several application areas. We present two convex optimization-based frameworks, a primal approach and a dual approach, to efficiently learn a non-parametric choice model from data that is close to the best-fitting one. As opposed to the existing literature, both approaches enjoy provable convergence guarantees and extend naturally to the dynamic observation setting. Our computational study on the dynamic setting reveals the true impact of how much data are needed and at what rate to achieve the best trade-off in terms of estimation accuracy and model simplicity. In the static setup, we also compare our non-parametric approach with existing parametric approaches.

Keywords: non-parametric choice model, conjugacy, duality, online convex optimization

Category 1: Applications -- OR and Management Sciences (Marketing )

Category 2: Convex and Nonsmooth Optimization (Convex Optimization )


Download: [PDF]

Entry Submitted: 02/18/2017
Entry Accepted: 02/19/2017
Entry Last Modified: 02/21/2018

Modify/Update this entry

  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository


Coordinator's Board
Classification Scheme
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society