Correlated Component Regression: Re-thinking Regression in the Presence of Near Collinearity
نویسنده
چکیده
We introduce a new regression method – called Correlated Component Regression (CCR) – which provides reliable predictions even with near multicollinear data. Near multicollinearity occurs when a large number of correlated predictors and relatively small sample size exists as well as situations involving a relatively small number of correlated predictors. Different variants of CCR are tailored to different types of regression (e.g., linear, logistic, Cox regression). We also present a step-down variable selection algorithm for eliminating irrelevant predictors. Unlike PLS-R and penalized regression approaches, CCR is scale invariant. CCR is illustrated in several examples involving real data and its performance is compared with other approaches using simulated data 1.
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