When building prediction models in the semiconductor environment, many variables, such as input/output variables, have causal relationships which may lead to multicollinearity. There are several approaches to address multicollinearity: variable elimination, orthogonal transformation, and adoption of biased estimates. This paper reviews these methods with respect to an application that has a structure more complex than simple pairwise correlations. We also present two algorithmic variable elimination approaches and compare their performance with that of the existing principal component regression and ridge regression approaches in terms of residual mean square and R2.
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