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

dc.contributor.author	Li, Ping
dc.contributor.author	Abowd, John M.
dc.date.accessioned	2017-05-30T16:28:53Z
dc.date.available	2017-05-30T16:28:53Z
dc.date.issued	2014-02-05
dc.description.abstract	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.	en_US
dc.description.sponsorship	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)255-5014	en_US
dc.identifier.uri	https://hdl.handle.net/1813/50074
dc.language.iso	en	en_US
dc.title	Boosting Models for Edit, Imputation and Prediction of Multiple Response Outcomes	en_US
dc.type	presentation	en_US

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