Data Fusion and File Grafting

Abstract

We present data fusion and file grafting as two complementary statistical techniques to relate independent data sets having a common group of variables and to obtain knowledge about the non common variables. 1.1 Introduction Data fusion is one of the most challenging issues that the statistical community would face in the next years. Since the response rate in questionnaires polls is decreasing, it seems natural not to overburden the task of respondents. One possible way is to work with two sources of data instead of a single one. The typical problem of data fusion consists on having one data file with complete information whereas the other data file has a certain number of variables missing. The objective of the fusion will be to estimate these latter variables from the knowledge adquired in the first data set. Thus, the problem of data fusion is a problem of prediction for a entire block of missing data. We can solve this problem with a great variety of techniques, for example, by ..