Predictive Analytics with Strategically Missing Data

Juheng Zhang (), Xiaoping Liu () and Xiao-Bai Li ()
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Juheng Zhang: Department of Operations and Information Systems, University of Massachusetts, Lowell, Massachusetts 01854;
Xiaoping Liu: D’Amore-McKim School of Business, Northeastern University, Boston, Massachusetts 02115
Xiao-Bai Li: Department of Operations and Information Systems, University of Massachusetts, Lowell, Massachusetts 01854;

INFORMS Journal on Computing, 2020, vol. 32, issue 4, 1143-1156

Abstract: We study strategically missing data problems in predictive analytics with regression. In many real-world situations, such as financial reporting, college admission, job application, and marketing advertisement, data providers often conceal certain information on purpose in order to gain a favorable outcome. It is important for the decision-maker to have a mechanism to deal with such strategic behaviors. We propose a novel approach to handle strategically missing data in regression prediction. The proposed method derives imputation values of strategically missing data based on the Support Vector Regression models. It provides incentives for the data providers to disclose their true information. We show that with the proposed method imputation errors for the missing values are minimized under some reasonable conditions. An experimental study on real-world data demonstrates the effectiveness of the proposed approach.

Keywords: business analytics; information disclosure; data manipulation; support vector regression; strategic learning. (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:32:y:4:i:2020:p:1143-1156

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