NCRN Meeting Spring 2016: Itemwise Missing at Random Modeling for Incomplete Multivariate Data

dc.contributor.author	Sadinle, Mauricio
dc.contributor.author	Reiter, Jerry
dc.date.accessioned	2016-05-13T14:27:06Z
dc.date.available	2016-05-13T14:27:06Z
dc.date.issued	2016-05-09
dc.description	Presented at the NCRN Meeting Spring 2016 in Washington DC on May 9-10, 2016; see http://www.ncrn.info/event/ncrn-spring-2016-meeting	en_US
dc.description.abstract	Modeling multivariate data that are subject to missingness requires making assumptions about how the missing data arise. We introduce the concept of the missing data being itemwise missing at random (IMAR) when each random variable is conditionally independent of its missingness indicator given the remaining variables and their missingness indicators. We show that this assumption leads to a non-parametric saturated class of models and illustrate how to use it with a number of examples. We also show how to perform sensitivity analysis and explore how the IMAR assumption can be relaxed using marginal information from auxiliary sources.	en_US
dc.description.sponsorship	NSF Grant 1507241 (NCRN Coordinating Office) and Award Number:1131897 (to Duke University)	en_US
dc.identifier.uri	https://hdl.handle.net/1813/43892
dc.language.iso	en_US	en_US
dc.subject	multivariate	en_US
dc.subject	Missing data	en_US
dc.subject	missingness	en_US
dc.title	NCRN Meeting Spring 2016: Itemwise Missing at Random Modeling for Incomplete Multivariate Data	en_US
dc.type	presentation	en_US

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