Paper Info
Title
PC Translation Models for Random Vectors and Multivariate Extremes.
Abstract
A novel class of models, referred to as polynomial chaos (PC) translation models, is developed for non-Gaussian vectors. The models match target marginal distributions exactly and dependence structures approximately. They are nonlinear transformations of truncated PC expansions whose coefficients are selected to best describe specified quantities of interest. Optimization algorithms are used to implement PC translation models. The computation demands to implement PC translation models and truncated PC expansions are similar. Numerical examples are presented to illustrate the implementation and performance of PC translation models.
Year
DOI
Venue
2019
10.1137/18M118061X
SIAM JOURNAL ON SCIENTIFIC COMPUTING
Keywords
Field
DocType
dependence,Monte Carlo,polynomial chaos,tail independence,spectral measure
Applied mathematics,Monte Carlo method,Spectral measure,Nonlinear system,Mathematical analysis,Multivariate statistics,Polynomial chaos,Optimization algorithm,Marginal distribution,Mathematics,Computation
Journal
Volume
Issue
ISSN
41
2
1064-8275
Citations 
PageRank 
References 
0
0.34
0
Authors
1
Name
Order
Citations
PageRank
Mircea Grigoriu143.83