【摘 要】
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We propose a novel approach to dimension reduction and variable selection based on localization.The method,to be called k-Nearest-Neighbor Sliced Inverse Re
【机 构】
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University of Georgia
【出 处】
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2009 International Conference on Financial Statistics and Fi
论文部分内容阅读
We propose a novel approach to dimension reduction and variable selection based on localization.The method,to be called k-Nearest-Neighbor Sliced Inverse Regression (KNN-SIR),is developed to relax the restrictive linearity condition on the predictor,and to achieve exhaustive estimation of the dimension reduction space.Together with this estimator,we also introduce a testing procedure to estimate dimensionality,and a variable selection procedure to handle sparsity in the predictors.Finally,a fast algorithm of KNN-SIR is developed,which is especially useful for huge data sets.We demonstrate the efficacy of our method by simulation and on real applications.
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