Regression-Based Data Fusion: Robust Residual Imputation

• Activity Number: 85
• Type: Contributed
• Date/Time: Sunday, August 1, 2010 : 4:00 PM to 5:50 PM
• Sponsor: Section on Survey Research Methods
• Author(s): Chris Moriarity*+ and Fritz Scheuren
• Companies: National Center for Health Statistics and NORC
• Keywords: statistical matching

Abstract

We have described a method (2001, 2003, 2004, 2009) for merging two independent samples using data fusion (also known as statistical matching). One sample contains (X,Z) and the other contains (X,Y), both drawn from a common nonsingular normal (X,Y,Z) distribution. Following Kadane (1978) and Rubin (1986), we employ regression in our approach. We assess the uncertainty introduced during the merge that is due to the unobserved (Y,Z) relationship by repetition over a range of (Y,Z) values that are consistent with the observed data. An essential part of our algorithm is to add random residuals to the regression estimates. Our initial approach for estimating the residual variance could fail (be negative) because it used subtraction of estimates from both files. Innovations due to D'Orazio, et al. (2006) and Kiesl and Rassler (2009) give improved results, yielding a robust algorithm.