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dc.contributor.authorMuhammad Hafiz Mohd Azizen_US
dc.identifier.citationMuhammad Hafiz Mohd Aziz (2022). Estimating mixture models in consumer segmentation. Final Year Project (FYP), Nanyang Technological University, Singapore.
dc.description.abstractMixture models are used in many fields to identify different sources of uncertainty. Market demand is an accumulation of each individuals' choice probabilities. Consumers with different preferences will be have different choice models. It is thus required to estimate the underlying choice models and the mixture proportion to accurately predict true market demand. Various attempts in literature struggled to find a balance between prediction accuracy and model interpretability. This paper is motivated by an algorithm in a recent paper which proposes a non-parametric estimation method based on the Frank-Wolfe algorithm to segment consumers and further apply the calibrated consumer segmentation to a price optimization problem, an important application in revenue management.en_US
dc.publisherNanyang Technological Universityen_US
dc.titleEstimating mixture models in consumer segmentationen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorYan Zhenzhenen_US
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.description.degreeBachelor of Science in Mathematical Sciencesen_US
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Appears in Collections:SPMS Student Reports (FYP/IA/PA/PI)
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