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Activity Number: 397
Type: Invited
Date/Time: Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
Sponsor: Survey Research Methods Section
Abstract - #307425
Title: What Statistical Problems Are Not Missing-Data Problems?
Author(s): Xiao-Li Meng*+
Companies: Harvard University
Keywords: ancillary statistics ; fiducial inference ; incomplete data ; structural inference ; latent variable
Abstract:

The answer to this rhetorical question can be derived from the abstract of my Y2K vignette "Missing Data: Dial M for ???" (JASA, 2000):

"Over the past century, statisticians and other scientists not only have invented numerous methods for handling miss ing/incomplete data, but also have invented many forms of missing data, including data augmentation, hidden states, latent variables, potential outcome, and auxiliary variables. Purposely constructing unobserved/unobservable variables offers an extraordinarily flexible and powerful framework for both scientific modeling and computation and is one of the central statistical contributions to natural, engineering, and social sciences. In parallel, much research has been devoted to better understanding and modeling of real-life missing-data mechanisms; that is, the unintended data selection process that prevents us from observing our intended data."

Whereas some may question whether I am committing the common sin of finding nails to fit one's favorite hammer, I am happy to report in this talk how the missing-data hammer is knocking down an old wall: the myth and mystery surrounding the fiducial inference. (Joint work with K. Liu)


Authors who are presenting talks have a * after their name.

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