Moment Based Dimension Reduction for Multivariate Response Regression
نویسندگان
چکیده
Dimension reduction aims to reduce the complexity of a regression without requiring a pre-specified model. In the case of multivariate response regressions, covariance-based estimation methods for the k-th moment based dimension reduction subspaces circumvent slicing and nonparametric estimation so that they are readily applicable to multivariate regression settings. In this article, the covariancebased method developed by Yin and Cook (2002) for univariate regressions is extended to multivariate response regressions and a new method is proposed. Simulated and real data examples illustrating the theory are presented.
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