Predictive Analytics with Strategically Missing Data

• Published in: INFORMS journal on computing 2020-01, Vol.32 (4), p.1143-1156, Article ijoc.2019.0947
• Main Authors: Zhang, Juheng, Liu, Xiaoping, Li, Xiao-Bai
• Format: Article
• Language: English
• Subjects: Applications; business analytics; College admissions; data manipulation; Decision making; Financial disclosure; Financial reporting; Incentives; information disclosure; Marketing; Missing data; Predictive analytics; Regression analysis; Regression models; strategic learning; Support vector machines; support vector regression
• ISSN: 1091-9856; 1526-5528; 1091-9856
• DOI: 10.1287/ijoc.2019.0947

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.