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Bayesian Semiparametric Regression: An Exposition and Application to Print Advertising Data

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  • Smith, M.
  • Mathur, S.
  • Kohn, R.

Abstract

A new regression based approach is proposed for modeling marketing databases. The approach is Bayesian and provides a number of significant improvements over current methods. Independent variables can enter into the model in either a parametric or nonparametric manner, significant variables can be identified from a large number of potential regressors and an appropriate transformation of the dependent variable can be automatically selected from a discrete set of pre-specified candidate transformations.

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Paper provided by Australian Graduate School of Management in its series Statistics Working Paper with number _010.

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Handle: RePEc:wop:agsmst:_010

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Postal: Sydney, NSW 2052
Web page: http://www.agsm.edu.au/
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  1. Smith, M. & Sheather S. & Kohn, R., . "Finite sample performance of robust Bayesian regression," Statistics Working Paper, Australian Graduate School of Management _011, Australian Graduate School of Management.
  2. Smith, M. & Kohn, R., . "Nonparametric Regression using Bayesian Variable Selection," Statistics Working Paper, Australian Graduate School of Management _009, Australian Graduate School of Management.
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Cited by:
  1. Huhmann, Bruce A. & Franke, George R. & Mothersbaugh, David L., 2012. "Print advertising: Executional factors and the RPB Grid," Journal of Business Research, Elsevier, Elsevier, vol. 65(6), pages 849-854.
  2. Danaher, Peter J. & Dagger, Tracey S. & Smith, Michael S., 2011. "Forecasting television ratings," International Journal of Forecasting, Elsevier, Elsevier, vol. 27(4), pages 1215-1240, October.

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