Nonparametric variance-based methods of assessing uncertainty importance

This paper examines the feasibility and value of using nonparametric variance-based methods to supplement parametric regression methods for uncertainty analysis of computer models. It shows from theoretical considerations how the usual linear regression methods are a particular case within the general framework of variance-based methods. Examples of strengths and weaknesses of the methods are demonstrated analytically and numerically in an example. The paper shows that relaxation of linearity assumptions in nonparametric variance-based methods comes at the cost of additional computer runs.