A Generalized ANOVA Dimensional Decomposition for Dependent Probability Measures
For researchers in uncertainty quantification and sensitivity analysis, this provides a generalized framework for handling dependent inputs, which is an incremental extension of classical ADD.
This paper extends ANOVA dimensional decomposition to dependent random variables, deriving component functions with zero means and hierarchical orthogonality. It proposes a constructive method using measure-consistent orthogonal polynomials and demonstrates that correlation structures significantly alter sensitivity indices and variable rankings.
This article explores the generalized analysis-of-variance or ANOVA dimensional decomposition (ADD) for multivariate functions of dependent random variables. Two notable properties, stemming from weakened annihilating conditions, reveal that the component functions of the generalized ADD have \emph{zero} means and are hierarchically orthogonal. By exploiting these properties, a simple, alternative approach is presented to derive a coupled system of equations that the generalized ADD component functions satisfy. The coupled equations, which subsume as a special case the classical ADD, reproduce the component functions for independent probability measures. To determine the component functions of the generalized ADD, a new constructive method is proposed by employing measure-consistent, multivariate orthogonal polynomials as bases and calculating the expansion coefficients involved from the solution of linear algebraic equations. New generalized formulae are presented for the second-moment characteristics, including triplets of global sensitivity indices, for dependent probability distributions. Furthermore, the generalized ADD leads to extended definitions of effective dimensions, reported in the current literature for the classical ADD. Numerical results demonstrate that the correlation structure of random variables can significantly alter the composition of component functions, producing widely varying global sensitivity indices and, therefore, distinct rankings of random variables. An application to random eigenvalue analysis demonstrates the usefulness of the proposed approximation.