Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Centralized and Competitive Inventory Models with Demand Substitution
Operations Research
The Effect of Product Assortment Changes on Customer Retention
Marketing Science
Inventory Record Inaccuracy: An Empirical Analysis
Management Science
Deconstructing Each Item's Category Contribution
Marketing Science
OM Practice---Choice-Based Revenue Management: An Empirical Study of Estimation and Optimization
Manufacturing & Service Operations Management
Structural Estimation of the Effect of Out-of-Stocks
Management Science
Computing Bid Prices for Revenue Management Under Customer Choice Behavior
Manufacturing & Service Operations Management
Clearance Pricing Optimization for a Fast-Fashion Retailer
Operations Research
A Nonparametric Approach to Modeling Choice with Limited Data
Management Science
Clearance Pricing Optimization for a Fast-Fashion Retailer
Operations Research
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We propose a method for estimating substitute and lost demand when only sales and product availability data are observable, not all products are displayed in all periods (e.g., due to stockouts or availability controls), and the seller knows its aggregate market share. The model combines a multinomial logit (MNL) choice model with a nonhomogeneous Poisson model of arrivals over multiple periods. Our key idea is to view the problem in terms of primary (or first-choice) demand; that is, the demand that would have been observed if all products had been available in all periods. We then apply the expectation-maximization (EM) method to this model, and we treat the observed demand as an incomplete observation of primary demand. This leads to an efficient, iterative procedure for estimating the parameters of the model. All limit points of the procedure are provably stationary points of the incomplete data log-likelihood function. Every iteration of the algorithm consists of simple, closed-form calculations. We illustrate the effectiveness of the procedure on simulated data and two industry data sets.