Estimation of Choice-Based Models Using Sales Data from a Single Firm

@article{Newman2014EstimationOC,
  title={Estimation of Choice-Based Models Using Sales Data from a Single Firm},
  author={Jeffrey P. Newman and Mark E. Ferguson and Laurie A. Garrow and Timothy L. Jacobs},
  journal={Manuf. Serv. Oper. Manag.},
  year={2014},
  volume={16},
  pages={184-197}
}
We develop a parameter estimation routine for multinomial logit discrete choice models in which one alternative is completely censored, i.e., when one alternative is never observed to have been chosen in the estimation data set. Our method is based on decomposing the log-likelihood function into marginal and conditional components. Our method is computationally efficient, provides consistent parameter estimates, and can easily incorporate price and other product attributes. Simulations based on… Expand
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References

SHOWING 1-10 OF 41 REFERENCES
Estimating Discrete Choice Models with Incomplete Data
In this paper a new approach is developed for estimating discrete choice modeling parameters for data sets in which one of the alternatives is never observed to have been chosen. This estimationExpand
Application of discrete choice models to choice-based revenue management problems: A cautionary note
Choice-based revenue management algorithms directly integrate a discrete choice model of customer behavior into the optimization function. In the classic formulation by Talluri and van Ryzin (TvR),Expand
Estimating unconstrained demand rate functions using customer choice sets
A good demand forecast should be at the heart of every revenue management model. Yet most demand models focus on product demand and do not incorporate customer choice behavior under offeredExpand
OM Practice - Choice-Based Revenue Management: An Empirical Study of Estimation and Optimization
TLDR
This study suggests that choice-based revenue management is both feasible to execute and economically significant in real-world airline environments. Expand
Estimating Primary Demand for Substitutable Products from Sales Transaction Data
TLDR
The expectation-maximization (EM) method is applied to this model, and the observed demand is treated as an incomplete observation of primary demand, which leads to an efficient, iterative procedure for estimating the parameters of the model. Expand
Estimating Primary Demand for Substitutable Products from Sales Transaction Data
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 stock-outs orExpand
A Column Generation Algorithm for Choice-Based Network Revenue Management
TLDR
To solve the CDLP for real-size networks, it is proved that the associated column generation subproblem is indeed NP-hard and a simple, greedy heuristic is proposed to overcome the complexity of an exact algorithm. Expand
Numerical Analysis of Effect of Sampling of Alternatives in Discrete Choice Models
A multitude of alternatives characterize the choice set in many activity and travel choice contexts. Analysts generally sample alternatives from the choice set in such situations because estimatingExpand
The Estimation of Choice Probabilities from Choice Based Samples
Ti-H CONCERN of this paper is the estimation of the parameters of a probabilistic choice model when choices rather than decision makers are sampled. Existing estimation methods presuppose anExpand
Demand Estimation Under Incomplete Product Availability
TLDR
A new dataset from a wireless inventory system installed on 54 vending machines to track product availability is studied, finding significant differences in demand estimates and the corrected model predicting significantly larger impacts of stock-outs on profitability. Expand
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