Decorrelating effects in multiple linear regression to decompose and attribute risk to common and proper effects
Abstract
Effects in multiple linear regression may be decorrelated to decompose and attribute risk to common and proper effects. In other words, an attribute risk may be decomposed to two or more causes, where each cause is characterized by multiple attributes. The risk decomposition may decompose risk into a first residual part associated with a first set of risk factors, a second residual part associated with a second set of risk factors, and a common part associated with a set of common hidden variables that minimize a correlation between the first set of factors and the second set of factors. The common hidden variables may be modeled using a hidden factor model. An effect of the correlation may be minimized on the first set of risk factors and the second set of risk factors, and how correlated the terms of the risk decomposition are may be quantified.
 Inventors:
 Issue Date:
 Research Org.:
 Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 1771483
 Patent Number(s):
 10796258
 Application Number:
 16/103,452
 Assignee:
 Triad National Security, LLC (Los Alamos, NM)
 Patent Classifications (CPCs):

G  PHYSICS G06  COMPUTING G06F  ELECTRIC DIGITAL DATA PROCESSING
G  PHYSICS G06  COMPUTING G06N  COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
 DOE Contract Number:
 AC5206NA25396
 Resource Type:
 Patent
 Resource Relation:
 Patent File Date: 08/14/2018
 Country of Publication:
 United States
 Language:
 English
 Subject:
 97 MATHEMATICS AND COMPUTING
Citation Formats
Hengartner, Nicolas W., and CuellarHengartner, Leticia. Decorrelating effects in multiple linear regression to decompose and attribute risk to common and proper effects. United States: N. p., 2020.
Web.
Hengartner, Nicolas W., & CuellarHengartner, Leticia. Decorrelating effects in multiple linear regression to decompose and attribute risk to common and proper effects. United States.
Hengartner, Nicolas W., and CuellarHengartner, Leticia. Tue .
"Decorrelating effects in multiple linear regression to decompose and attribute risk to common and proper effects". United States. https://www.osti.gov/servlets/purl/1771483.
@article{osti_1771483,
title = {Decorrelating effects in multiple linear regression to decompose and attribute risk to common and proper effects},
author = {Hengartner, Nicolas W. and CuellarHengartner, Leticia},
abstractNote = {Effects in multiple linear regression may be decorrelated to decompose and attribute risk to common and proper effects. In other words, an attribute risk may be decomposed to two or more causes, where each cause is characterized by multiple attributes. The risk decomposition may decompose risk into a first residual part associated with a first set of risk factors, a second residual part associated with a second set of risk factors, and a common part associated with a set of common hidden variables that minimize a correlation between the first set of factors and the second set of factors. The common hidden variables may be modeled using a hidden factor model. An effect of the correlation may be minimized on the first set of risk factors and the second set of risk factors, and how correlated the terms of the risk decomposition are may be quantified.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {10}
}
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