Technical Note—An Expectation-Maximization Method to Estimate a Rank-Based Choice Model of Demand

DOI10.1287/opre.2016.1559zbMath1367.62329OpenAlexW2590545295MaRDI QIDQ5278273

Garrett J. van Ryzin, Gustavo J. Vulcano

Publication date: 13 July 2017

Published in: Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1287/opre.2016.1559

zbMATH Keywords

EM methoddemand estimationchoice behaviordemand untruncation

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Inventory, storage, reservoirs (90B05)

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Mallows-Smoothed Distribution over Rankings Approach for Modeling Choice ⋮ Assortment optimization under the sequential multinomial logit model ⋮ Robust assortment optimization under sequential product unavailability ⋮ In-out algorithm for assortment planning under a ranking-based consumer choice model ⋮ Product-closing approximation for ranking-based choice network revenue management ⋮ A gradient descent method for estimating the Markov chain choice model ⋮ The impact of seat resource fragmentation on railway network revenue management ⋮ The Approximability of Assortment Optimization Under Ranking Preferences ⋮ A review of choice-based revenue management: theory and methods ⋮ Technical Note—An Expectation-Maximization Algorithm to Estimate the Parameters of the Markov Chain Choice Model ⋮ A Markov Chain Approximation to Choice Modeling ⋮ Tobit Kalman filter with channel fading and dead-zone-like censoring

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