Recently, Yoo (Statistics 50:1086–1099, 2016) newly defines an informative predictor subspace to contain the central subspace. The method to estimate the informative predictor subspace does not require any of the conditions assumed to hold in usual sufficient dimension reduction methodologies. However, like sliced inverse regression (Li in J Am Stat Assoc 86:316–342, 1991) and sliced average variance estimation (Cook and Weisberg in J Am Stat Assoc 86:328–332, 1991), its non-asymptotic behavior in the estimation is sensitive to the choices of the categorization of the predictors and response. The paper develops an estimation approach that is robust to the categorization choices. For this, sample kernel matrices are combined in two ways. Numerical studies and real data analysis are presented to confirm the potential usefulness of the proposed approach in practice.

中文翻译：

---

中心子空间融合聚类均值估计

最近，Yoo（Statistics 50：1086-1099，2016）重新定义了一个信息丰富的预测子空间以包含中央子空间。估计信息量预测子空间的方法不需要任何假定的条件即可满足通常的充分降维方法。但是，就像切片逆回归（Li在J Am Stat Assoc 86：316–342，1991中）和切片平均方差估计（Cook and Weisberg在J Am Stat Assoc 86：328–332，1991）中一样，其非渐近行为在该估计对预测变量和响应的分类选择敏感。本文提出了一种对分类选择具有鲁棒性的估计方法。为此，样本内核矩阵以两种方式组合。