Data envelopment analysis (DEA) is a mathematical programming technique for identifying efficiency scores of decision making units (DMUs). Since DEA models cannot present efficient frontiers of PPS, in order to do this, we introduce a method for identifying efficient frontier for DMUs with interval data.

We investigate a continuous-time mean–variance portfolio selection problem. Different from the general stochastic dynamic programming approach, such as using Hamilton–Jacobi–Bellman (HJB) equation, this paper adopts the Lagrange duality method and the finite difference approach to derive explicit closed-form expressions for the efficient investment strategy and the mean–variance efficient front...