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Decorrelating effects in multiple linear regression to decompose and attribute risk to common and proper effects
Decorrelating effects in multiple linear regression to decompose and attribute risk to common and proper effects
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机译：在多元线性回归中解相关效应以分解风险并将其归因于常见和适当的效应
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摘要
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.
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