Estimating statistical moments of random systems based on appropriate reference variables. Issue 6 (7th August 2017)

• Record Type: Journal Article
• Authors: Fan, Wenliang; Yang, Pengchao; Wang, Yule; H.-S. Ang, Alfredo; Li, Zhengliang
• Is Part Of: Engineering computations. Volume 34:Issue 6(2017)
• Page Start: 2001
• Page End: 2030
• Publication Date: 2017-08-07
• Subjects: Dimension-reduction method (DRM) -- General Rosenblatt transformation -- Linear transformation -- Point estimate method (PEM) -- Reference variables -- Statistical moments; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-08-2016-0288
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: The purpose of this paper is to find an accurate, efficient and easy-to-implement point estimate method (PEM) for the statistical moments of random systems. Design/methodology/approach: First, by the theoretical and numerical analysis, the approximate reference variables for the frequently used nine types of random variables are obtained; then by combining with the dimension-reduction method (DRM), a new method which consists of four sub-methods is proposed; and finally, several examples are investigated to verify the characteristics of the proposed method. Findings: Two types of reference variables for the frequently used nine types of variables are proposed, and four sub-methods for estimating the moments of responses are presented by combining with the univariate and bivariate DRM. Research limitations/implications: In this paper, the number of nodes of one-dimensional integrals is determined subjectively and empirically; therefore, determining the number of nodes rationally is still a challenge. Originality/value: Through the linear transformation, the optimal reference variables of random variables are presented, and a PEM based on the linear transformation is proposed which is efficient and easy to implement. By the numerical method, the quasi-optimal reference variables are given, which is the basis of the proposed PEM based on the quasi-optimal reference variables, together with high efficiency and ease of implementation.