Boosting Models for Edit, Imputation and Prediction of Multiple Response Outcomes

In this paper, we propose a statistical framework that generalizes the classical logit model to predict multiple responses (i.e., multi-label classification). We develop an effective implementation based on boosting and trees. For the NCRN seminar we present an application to editing and imputation in the multiple response race and ethnicity coding on the American Community Survey.This research is funded by the National Science Foundation grant 1131848 (Cornell Census-NSF Research Node: Integrated Research Support, Training and Data Documentation). Prepared by Cornell University 373 Pine Tree Road Ithaca, NY 14850-2820 (607)255-501