Statistical Inference Equivalence Principle and its Applications

摘要

In this paper, we propose a new approach to solve constrained optimization problems under parameter uncertainty. This approach is based on the statistical inference equivalence principle, the idea of which belongs to the authors. It is especially effective when we deal with asymmetric loss functions and small data samples. The results obtained in this paper agree with the simulation results, which confirm the validity of the theoretical predictions of performance of the suggested approach.