Open AccessMetamodeling for Policy Simulations with Multivariate Outcomes
Author(s) -
Huaiyang Zhong,
Margaret L. Brandeau,
Golnaz Eftekhari Yazdi,
Jianbo Wang,
Shayla Nolen,
Liesl M. Hagan,
W. Thompson,
Sabrina A Assoumou,
Benjamin P. Linas,
Joshua A. Salomon
Publication year - 2022
Publication title -
medical decision making
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.393
H-Index - 103
eISSN - 1552-681X
pISSN - 0272-989X
DOI - 10.1177/0272989x221105079
Subject(s) - interpretability , hyperparameter , machine learning , computer science , feature selection , multivariate adaptive regression splines , kriging , artificial intelligence , metamodeling , multivariate statistics , regression analysis , bayesian multivariate linear regression , programming language
Metamodels are simplified approximations of more complex models that can be used as surrogates for the original models. Challenges in using metamodels for policy analysis arise when there are multiple correlated outputs of interest. We develop a framework for metamodeling with policy simulations to accommodate multivariate outcomes.