.png){kind=icon}
.png){kind=icon}
Return
Distribution-insensitive influential point detection for high dimensional regression model
.png){kind=icon}
PRE
AI
DOI：10.1007/s11222-026-10837-5.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Influential points can distort the statistical inference, leading to misleading conclusions. Hence, influential diagnosis is an important issue in data analysis, but is less studied for high-dimensional data. When there are multiple influential observations, dealing with masking and swamping effects is challenging, and existing methods often impose strong distributional assumptions on non-influential observations such as the normality assumption. Moreover, these methods are sensitive to these assumptions. In this paper, we propose a distribution-insensitive influential measure, based on the correlation between predictors and a transformation of the response, which relaxes the assumption on the underlying distribution of non-influential observations significantly. Particularly, no assumption is imposed on the response except that the response is continuous. Furthermore, a distribution-insensitive influential point (DIP) detection method is proposed, which is efficient in handling masking and swamping effects and robust to the distribution of non-influential observations. Theoretical properties of DIP are established. Simulation results and real data analysis support the theoretical findings.
Keywords:
Influential point
High-dimensional regression
Masking effects
Swamping effects
Distribution-insensitive
Journal
S
IF:
1.6
Papers:
200
Citations:
0