Minimax-regret sample design in anticipation of missing data, with application to panel data

Missing data problems are ubiquitous in data collection. In surveys, these problems may arise from unit response, item nonresponse, and panel attrition. Building on the Dominitz and Manski (2017) study of choice between two or more sampling processes that differ in cost and quality, we study minimax-regret sample design in anticipation of missing data, where the collected data will be used for prediction under square loss of the values of functions of two variables. The analysis imposes no assumptions that restrict unobserved outcomes. Findings are reported for prediction of the values of linear and indicator functions using panel data with attrition. We also consider choice between a panel and repeated cross sections.