Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/125212
Title: ESTIMATION AND OPTIMIZATION OF LOGIT DEMAND MODEL WITH COVARIATES, CENSORED DATA, AND AUXILIARY INFORMATION
Authors: LI BAIYU
Keywords: Logit model, estimation and optimization, censoring, covariates
Issue Date: 8-Mar-2016
Citation: LI BAIYU (2016-03-08). ESTIMATION AND OPTIMIZATION OF LOGIT DEMAND MODEL WITH COVARIATES, CENSORED DATA, AND AUXILIARY INFORMATION. ScholarBank@NUS Repository.
Abstract: We formulate a newsvendor model where demand depends on a set of stochastically changing covariates, and show that Bayesian estimation of this model, given a history of covariates, censored sales data, and auxiliary information such as customer traffic flow, can be implemented efficiently using a Metropolis-within-Gibbs Markov Chain Monte Carlo algorithm. We benchmark the impact of demand censoring, ignoring covariates, and incorporating traffic flow information on the out-of-sample performance of an optimal inventory decision. We show in an extended example that improvements in profit can be substantial, even if an incomplete set of covariates is being used, and that initiatives to improve demand forecasting by collecting relevant side information and combining multiple data sets can have a substantial impact on operational efficiency.
URI: http://scholarbank.nus.edu.sg/handle/10635/125212
Appears in Collections:Master's Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
Li Baiyu.pdf957.54 kBAdobe PDF

OPEN

NoneView/Download

Page view(s)

138
checked on Sep 22, 2022

Download(s)

142
checked on Sep 22, 2022

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.