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Multi level categorical data fusion using partially fused data

Author

Listed:
  • Zvi Gilula

    (Hebrew University)

  • Robert McCulloch

    (University of Chicago Booth School of Business)

Abstract

Data fusion poses challenging methodological issues for inferring the joint distribution of two random variables when the information available is mainly confined to the marginal distributions. When the variables are categorical, the challenges are even more severe. Applications of categorical data fusion are of top importance in marketing, especially in advertising. A great deal of categorical data fusion methods are confined to binary variables. In this paper we develop an innovative approach to categorical data fusion that extends previous methodologies and applies to categorical variables with any number of levels. We introduce a new concept of “evident dependence” that describes a variety of patterns of joint distributions given the marginals. Using information from partially fused data, our method smoothly accommodates a Bayesian approach based on mixtures of joint distributions constructed using evident dependence. The approach is illustrated using data from the advertising industry.

Suggested Citation

  • Zvi Gilula & Robert McCulloch, 2013. "Multi level categorical data fusion using partially fused data," Quantitative Marketing and Economics (QME), Springer, vol. 11(3), pages 353-377, September.
    • Handle: RePEc:kap:qmktec:v:11:y:2013:i:3:d:10.1007_s11129-013-9136-0
    DOI: 10.1007/s11129-013-9136-0
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    References listed on IDEAS

    as
    1. Kiesl, Hans & Rässler, Susanne, 2006. "How valid can data fusion be?," IAB-Discussion Paper 200615, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    2. Rubin, Donald B, 1986. "Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 87-94, January.
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    Cited by:

    1. Zvi Gilula & Robert E. McCulloch & Yaacov Ritov & Oleg Urminsky, 2019. "A study into mechanisms of attitudinal scale conversion: A randomized stochastic ordering approach," Quantitative Marketing and Economics (QME), Springer, vol. 17(3), pages 325-357, September.
    2. Hyowon Kim & Greg M. Allenby, 2022. "Integrating Textual Information into Models of Choice and Scaled Response Data," Marketing Science, INFORMS, vol. 41(4), pages 815-830, July.
    3. Gessendorfer Jonathan & Beste Jonas & Drechsler Jörg & Sakshaug Joseph W., 2018. "Statistical Matching as a Supplement to Record Linkage: A Valuable Method to Tackle Nonconsent Bias?," Journal of Official Statistics, Sciendo, vol. 34(4), pages 909-933, December.
    4. Rajkumar Venkatesan & Alexander Bleier & Werner Reinartz & Nalini Ravishanker, 2019. "Improving customer profit predictions with customer mindset metrics through multiple overimputation," Journal of the Academy of Marketing Science, Springer, vol. 47(5), pages 771-794, September.

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    More about this item

    Keywords

    Evident dependence; Mixture modeling; Copulas;
    All these keywords.

    JEL classification:

    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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