A Semiparametric Approach to Dimension Reduction.
A Semiparametric Approach to Dimension Reduction.
复制标题
降维的半参数方法
DOI：
10.1080/01621459.2011.646925
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发表时间：
2012
影响因子：
3.7
通讯作者：
Zhu L
中科院分区：
文献类型：
--
作者：
Ma Y;Zhu L
我们提供了一个新的和完全不同的方法来降维问题从现有的文献。我们投降维问题的半参数估计框架，并推导出估计方程。从新的角度看待这个问题，使我们能够得到一个丰富的估计类，并获得经典的降维技术作为这类估计的特殊情况。半参数方法还表明，在逆回归上下文中，同时保持估计结构的完整性，共同的假设线性和/或恒定方差的协变量可以删除的成本进行额外的非参数回归。通过模拟研究和一个真实的数据例子说明了没有这些共同假设的半参数估计。这篇文章有在线补充材料。
We provide a novel and completely different approach to dimension-reduction problems from the existing literature. We cast the dimension-reduction problem in a semiparametric estimation framework and derive estimating equations. Viewing this problem from the new angle allows us to derive a rich class of estimators, and obtain the classical dimension reduction techniques as special cases in this class. The semiparametric approach also reveals that in the inverse regression context while keeping the estimation structure intact, the common assumption of linearity and/or constant variance on the covariates can be removed at the cost of performing additional nonparametric regression. The semiparametric estimators without these common assumptions are illustrated through simulation studies and a real data example. This article has online supplementary material.