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Efficient Experimental Designs for Hyperparameter Estimation
Qing Liu
Angela Dean
David Bakken
Greg M. Allenby
其他書名
Learning When Effect-Sizes are Large
出版
SSRN
, 2007
URL
http://books.google.com.hk/books?id=PtbczwEACAAJ&hl=&source=gbs_api
註釋
Research in marketing, and business in general, involves understanding when effect-sizes are expected to be large and when they are expected to be small. Understanding the contexts in which consumers are sensitive to offers, and variables such as price, is an important aspect of merchandising, selling and promotion. In this paper, we propose efficient methods of learning about contextual factors that influence consumer preference and sensitivities within the context of a hierarchical Bayes model. A design criterion is developed for hierarchical linear models, and validated in a study of the level effect in conjoint analysis using a national sample of respondents. Extensions to other model structures are discussed.
