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dc.contributor.authorLi, Ping
dc.contributor.authorAbowd, John M.
dc.date.accessioned2017-05-30T16:28:53Z
dc.date.available2017-05-30T16:28:53Z
dc.date.issued2014-02-05
dc.identifier.urihttps://hdl.handle.net/1813/50074
dc.description.abstractIn 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.en_US
dc.description.sponsorshipThis 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)255-5014en_US
dc.language.isoenen_US
dc.titleBoosting Models for Edit, Imputation and Prediction of Multiple Response Outcomesen_US
dc.typepresentationen_US


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