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 a generic convex optimization-based framework to efficiently learn a simple non-parametric choice model from data that is close to the best-fitting one, where simplicity is measured by sparsity of the underlying distribution. As opposed to the existing literature, we propose a solution method which enjoys provable convergence guarantees and extends 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.

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 )


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Entry Submitted: 02/18/2017
Entry Accepted: 02/19/2017
Entry Last Modified: 06/01/2017

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