Robust data analysis in innovation project portfolio management

Abstract

The paper states the mathematical model of portfolio management that allows to create an effective portfolio of innovation projects. Within the framework of this model the robust approach to data analysis is applied and expanded for the tasks of regression analysis of project data. The approach of robust estimation of regression parameters based on the maximum likelihood method in case of arbitrary contamination is suggested. A number of heuristic algorithms for estimating regression parameters in the case of symmetric data contamination is reviewed and modified.