BAYESIAN FINITE POPULATION IMPUTATION FOR DATA FUSION

Abstract: In data fusion, data owners seek to combine datasets with disjoint observations and distinct variables to estimate relationships among the variables. One approach is to concatenate the files, specify models relating the variables not jointly observed, and use the models to generate multiple imputations of the missing data. We show that the standard multiple imputation estimator of the sampling variance can have positive bias in such contexts. We present an approach for correcting this problem based on Bayesian finite population inference. We also present an approach for data fusion when some values are confidential and cannot be shared

Paradigmatic approach to managing complexity in knowledge-driven marketing

This research develops a measure of Customer Lifetime Value, extending existing measures, that provides organisations having large customer databases the means to place a financial value on customers. Using this measure, new models are developed that provide a framework to use knowledge to drive marketing decisions