Decorrelating effects in multiple linear regression to decompose and attribute risk to common and proper effects
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.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC52-06NA25396
- Assignee:
- Triad National Security, LLC (Los Alamos, NM)
- Patent Number(s):
- 10,796,258
- Application Number:
- 16/103,452
- OSTI ID:
- 1771483
- Resource Relation:
- Patent File Date: 08/14/2018
- Country of Publication:
- United States
- Language:
- English
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