Meta-analyses using information reweighting: An application to online advertising

Pengyuan Wang (), Eric Bradlow and Edward George

Quantitative Marketing and Economics (QME), 2014, vol. 12, issue 2, 209-233

Abstract: Because technology-enabled marketing research has led to information arriving at a rapid pace, methods in marketing that allow for coherent, sequential and fast information integration are needed. We propose in this research a new approach to information integration: Information Reweighted Priors (IRPs). It is a sample reweighting approach which utilizes the output from a Bayesian model fit using Markov Chain Monte Carlo, with no restrictions on the likelihood, prior distributions, or data structure; hence a general purpose tool. We demonstrate the approach with simulated datasets and an online advertising dataset with external information obtained from i) previous advertising studies in the industry from a major online advertising portal, ii) past academic studies of online adverting and iii) out-of-sample summaries of the dataset. Copyright Springer Science+Business Media New York 2014

Keywords: Information integration; Prior reweighting; Advertising; Informative priors; Statistical computing; C11 - Bayesian Analysis: General; M31 - Marketing; M37 - Advertising (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:kap:qmktec:v:12:y:2014:i:2:p:209-233

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DOI: 10.1007/s11129-014-9145-7

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