.png){kind=icon}
返回
Groupwise Dimension Reduction
.png){kind=icon}
PRE
AI
DOI：10.1198/jasa.2010.tm09643.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In many regression applications, the predictors fall naturally into a number of groups or domains, and it is often desirable to establish a domain-specific relation between the predictors and the response. In this article, we consider dimension reduction that incorporates such domain knowledge. The proposed method is based on the derivative of the conditional mean, where the differential operator is constrained to the form of a direct sum. This formulation also accommodates the situations where dimension reduction is focused only on part of the predictors; as such it extends Partial Dimension Reduction to cases where the blocked predictors are continuous. Through simulation and real data analyses, we show that the proposed method achieves greater accuracy and interpretability than the dimension reduction methods that ignore group information. Furthermore, the new method does not require the stringent conditions on the predictor distribution that are required by existing methods.
Keyword:
Central mean subspace
Direct sum of differential operators
Minimum average variance estimation
Outer product estimator
Partial dimension reduction
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
J
IF:
3
论文数:
5.2K
被引数:
4.8W
机构
引用论文
Sufficient dimension reduction in regressions with categorical predictors.png)
ANNALS OF STATISTICS
OAAI
ANNALS OF STATISTICSIF3.7
OAAI分享收藏Possibilities and Limitations of Spatially Explicit Site Index Modelling for Spruce Based on National Forest Inventory Data and Digital Maps of Soil and Climate in Bavaria (SE Germany)ForestsOAAI
ForestsIF0
OAAI分享收藏
分享收藏Impact of CO2 influx on sandstone reservoir quality: A case study of the Quantou Formation, southern Songliao Basin, ChinaCO2注入对砂岩储层质量的影响：以中国松辽盆地南部的泉头组为例
PREAI
分享收藏
查看更多内容