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Statistical Issues in Predictive Modeling

  • Robert C. Blattberg
  • Byung-Do Kim
  • Scott A. Neslin
Part of the International Series in Quantitative Marketing book series (ISQM, volume 18)

Abstract

Whereas Chapter 10 describes the basic process of predictive modeling, this chapter goes into depth on three key issues: selection of variables, treatment of missing data, and evaluation of models. Topics covered include stepwise selection and principal components methods of variable selection; imputation methods, missing variable dummies, and data fusion techniques for missing data; and validation techniques and metrics for evaluating predictive models.

Keywords

Validation Sample Lorenz Curve Principal Component Regression Default Probability Single Imputation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Robert C. Blattberg
    • 1
    • 2
  • Byung-Do Kim
    • 3
  • Scott A. Neslin
    • 4
  1. 1.Kellogg School of ManagementNorthwestern UniversityEvanstonUSA
  2. 2.Tepper School of BusinessCarnegie-Mellon UniversityPittsburghUSA
  3. 3.Graduate School of BusinessSeoul National UniversitySeoulKorea
  4. 4.Tuck School of BusinessDartmouth CollegeHanoverUSA

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