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Data augmentation for latent variables in marketing

Kao, Ling-Jing
http://rave.ohiolink.edu/etdc/view?acc_num=osu1155653751

Abstract Details

2006, Doctor of Philosophy, Ohio State University, Business Administration.
Latent variable models are an important aspect of consumer research whenever the determinants of behavior are an important aspect of study. The purpose of this thesis is to develop a new method of error augmentation to deal with latent variable models of heterogeneous, non-linear consumer behavior. This thesis comprises three essays. The first essay develops a new method of error augmentation for state-space models of economic behavior where the observed behavior is related to a latent variable whose temporal variation is described by a state equation. The proposed is applied to analyze a consumer’s purchase and resignation decisions in a membership club. The result indicates that increasing inter-arrival time between shipments can lead to longer customer longevity and greater sales. In the second essay, a state-space model is proposed to investigate the possibility to model inter-purchase times as an independent variable. The results indicate that the proposed state-space model can accurately describe customer behavior when the specification of the state equation is plausible for the data. In the third essay, a demand model is developed to treatment effects, line extension, and, the consumer decisions of brand-pack and no-choice for consumer packaged goods at the level of stock-keeping unit. The proposed model is illustrated with a panel data of a product category of consumer packaged goods. The results indicate that media can make some consumers have extreme preferences, and make preferences of some consumers become more homogeneous. This thesis contributes marketing literature by developing a new method of error augmentation for latent variable models that cannot be estimated by standard approaches. The new method of error augmentation is illustrated by three different marketing applications in this thesis.
Greg Allenby (Advisor)
233 p.
Business Administration, Marketing
marketing; Bayesian statistics; data augmentation; state-space models; choice models; consumer preference change; media effects

Recommended Citations

Citations

  • Kao, L.-J. (2006). Data augmentation for latent variables in marketing [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1155653751

    APA Style (7th edition)

  • Kao, Ling-Jing. Data augmentation for latent variables in marketing. 2006. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1155653751.

    MLA Style (8th edition)

  • Kao, Ling-Jing. "Data augmentation for latent variables in marketing." Doctoral dissertation, Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=osu1155653751

    Chicago Manual of Style (17th edition)

osu1155653751
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© 2006, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.