Sliced Inverse Regression (SIR) is a statistical technique used primarily for dimension reduction in multivariate data analysis, especially in the context of regression problems. Developed by Li in 1991, SIR is particularly useful when the relationship between the predictors (independent variables) and the response (dependent variable) is complex or high-dimensional.
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