Estimating mixture models in consumer segmentation
Mixture 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 model...
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| Format: | Final Year Project |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/156310 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Mixture 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. |
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