TY  - JOUR
T1  - Categorical data fusion using auxiliary information
JF  - Annals of Applied Statistics
Y1  - 2016
A1  - B. K. Fosdick
A1  - M. De Yoreo
A1  - J. P. Reiter
KW  - Imputation
KW  - Integration
KW  - Latent Class
KW  - Matching
AB  - In data fusion analysts seek to combine information from two databases comprised of disjoint sets of individuals, in which some variables appear in both databases and other variables appear in only one database. Most data fusion techniques rely on variants of conditional independence assumptions. When inappropriate, these assumptions can result in unreliable inferences. We propose a data fusion technique that allows analysts to easily incorporate auxiliary information on the dependence structure of variables not observed jointly; we refer to this auxiliary information as glue. With this technique, we fuse two marketing surveys from the book publisher HarperCollins using glue from the online, rapid-response polling company CivicScience. The fused data enable estimation of associations between people's preferences for authors and for learning about new books. The analysis also serves as a case study on the potential for using online surveys to aid data fusion.
VL  - 10
UR  - http://projecteuclid.org/euclid.aoas/1483606845
ER  -