  In this paper we develop a unified approach for solving a wide class of
sequential selection problems. This class includes, but is not limited to,
selection problems with no-information, rank-dependent rewards, and considers
both fixed as well as random problem horizons. The proposed framework is based
on a reduction of the original selection problem to one of optimal stopping for
a sequence of judiciously constructed independent random variables. We
demonstrate that our approach allows exact and efficient computation of optimal
policies and various performance metrics thereof for a variety of sequential
selection problems, several of which have not been solved to date.
