  We present a new model and methods for the posterior drift problem where the
regression function in the target domain is modeled as a linear adjustment (on
an appropriate scale) of that in the source domain, an idea that inherits the
simplicity and the usefulness of generalized linear models and accelerated
failure time models from the classical statistics literature, and study the
theoretical properties of our proposed estimator in the binary classification
problem. Our approach is shown to be flexible and applicable in a variety of
statistical settings, and can be adopted to transfer learning problems in
various domains including epidemiology, genetics and biomedicine. As a concrete
application, we illustrate the power of our approach through mortality
prediction for British Asians by borrowing strength from similar data from the
larger pool of British Caucasians, using the UK Biobank data.
