Year: 2023
Type: preprint
Abstract: We study the problem of estimating E(g(X)), where g is a real-valued function of d variables and X is a d-dimensional Gaussian vector with a given covariance matrix. We present a new unbiased estimator for E(g(X)) that combines the randomized dimension reduction technique with principal components analysis. Under suitable conditions, we prove that our algorithm outperforms the standard Monte Carlo method by a factor of order d.
Author: Kahale, Nabil
Institution: ESCP Business School
Language: English
Subfield: Numerical Analysis
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4366341349