Linear discrimination with strategically missing values

This study analyzes a problem where a decision maker needs to estimate missing values that are hidden strategically by agents so that further analysis can be carried out as if data are complete. Data can be missing due to different reasons. When data are provided by intelligent agents, most often information is hidden strategically to receive a favorable classification (or potential transaction) from the decision maker. Anticipating such strategic moves, we find a set of default vectors that the decision maker can use for replacing missing values, such that she minimizes her misclassification rate and incents agents to publish information at the same time. In theoretical and empirical studies, the performance of this set of default vectors is compared to some common statistical methods for handling missing data. Empirical results show that the default vectors chosen from this set dominates other methods in terms of misclassification rates. (Full text of this dissertation may be available via the University of Florida Libraries web site. Please check http://www.uflib.ufl.edu/etd.html)