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Combining related and sparse data in linear regression models

Author: Vanhonacker, Wilfried R. ; Lehmann, Donald ; Sultan, FareenaINSEAD Area: MarketingIn: Journal of Business and Economic Statistics, no. 8, July 1990 Language: EnglishDescription: p. 327-336.Type of document: INSEAD ArticleNote: Please ask the Library for this articleAbstract: Meta-analysis has become popular approach for studying systematic variation in parameter estimates across studies. This paper discusses the use of meta-analysis results as prior information in a new study. In contrast to hierarchical prior distributions in a traditional Bayesian framework which are characterized by complete exchangeability, meta-analysis priors explicitly incorporate heterogeneity in prior vectors. This paper discusses the nature of the meta-analysis priors, their properties, and shows how they can be integrated into a familiar recursive estimation framework so as to enhance the efficiency o parameter estimates in linear regression models. This approach has the added advantage that it can provide such estimates when the design or data matrix is not of full rank, or when observations are too few to allow independent estimation. The methodology is illustrated using published and new meta-analysis results in market response and diffusion of innovation models
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Meta-analysis has become popular approach for studying systematic variation in parameter estimates across studies. This paper discusses the use of meta-analysis results as prior information in a new study. In contrast to hierarchical prior distributions in a traditional Bayesian framework which are characterized by complete exchangeability, meta-analysis priors explicitly incorporate heterogeneity in prior vectors. This paper discusses the nature of the meta-analysis priors, their properties, and shows how they can be integrated into a familiar recursive estimation framework so as to enhance the efficiency o parameter estimates in linear regression models. This approach has the added advantage that it can provide such estimates when the design or data matrix is not of full rank, or when observations are too few to allow independent estimation. The methodology is illustrated using published and new meta-analysis results in market response and diffusion of innovation models

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