基于选择的联合估计的仿射子空间收缩法
Topic: |
An Affine-Subspace Shrinkage Approach to Choice-Based Conjoint Estimation |
Time&Date: |
10:30 am - 12:00 pm, May10, 2024 (Friday) |
Venue |
Room 603, Administration Building |
Zoom Link: |
https://cuhk-edu-cn.zoom.us/j/8913862860?pwd=WDdDbFBRbW9hSTVyTTRTancvbmI0dz09 |
Speaker: |
Prof. Yupeng Chen (Nanyang Technological University) |
Abstract: |
Firms routinely use choice-based conjoint (CBC) data to estimate consumer preferences. Since the amount of information elicited from each respondent is often limited, effective information pooling across respondents is critical for accurate CBC estimation. In this paper, we propose a novel affine-subspace shrinkage approach to pooling information in CBC estimation. Our approach, formulated as a simple and efficient convex optimization problem, models preference heterogeneity by shrinking the individual-level partworth estimates toward an affine subspace of the partworth space, which itself is selected as part of the estimation. Using an extensive CBC simulation experiment and two field CBC data sets, we show that our model outperforms a strong multitask learning model, and it performs comparably to a hierarchical Bayes model with a Dirichlet process prior which requires a considerably more sophisticated solution algorithm. |
Biography: |
Yupeng Chen is an Assistant Professor of Marketing at Nanyang Technological University in Singapore. He is primarily interested in developing new machine learning and optimization models for preference estimation, adaptive experimentation, and dynamic control. He is also interested in conducting field experiments to understand consumer behaviors and test retailing strategies. Prior to NTU, Yupeng obtained his Ph.D. in Marketing from Wharton. |