Identification of polynomial chaos representations in high dimension

The usual identification methods of polynomial chaos expansions in high dimension are based on the use of a series of truncations that induce numerical bias. We first quantify the detrimental influence of this numerical bias, we then propose a new decomposition of the polynomial chaos coefficients to allow performing relevant convergence analysis and identification with respect to an arbitrary measure for the high dimension case.