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Title: 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 Lab. (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 G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
G - PHYSICS G06 - COMPUTING G06Q - DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES
DOE Contract Number:  
AC52-06NA25396
Resource Type:
Patent
Resource Relation:
Patent File Date: 08/14/2018
Country of Publication:
United States
Language:
English

Citation Formats

Hengartner, Nicolas W., and Cuellar-Hengartner, 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., & Cuellar-Hengartner, Leticia. Decorrelating effects in multiple linear regression to decompose and attribute risk to common and proper effects. United States.
Hengartner, Nicolas W., and Cuellar-Hengartner, 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 Cuellar-Hengartner, 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}
}

Works referenced in this record:

Common risk factors in the returns on stocks and bonds
journal, February 1993


Exposure method and apparatus
patent, December 2000


Orthogonalized factors and systematic risk decomposition
journal, May 2013