.png){kind=icon}
Return
Robust linear model selection based on least angle regression
.png){kind=icon}
PRE
AI
DOI：10.1198/016214507000000950.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this article we consider the problem of building a linear prediction model when the number of candidate predictors is large and the data possibly contain anomalies that are difficult to visualize and clean. We want to predict the nonoutlying cases; therefore, we need a method that is simultaneously robust and scalable. We consider the stepwise least angle regression (LARS) algorithm which is computationally very efficient but sensitive to outliers. We introduce two different approaches to robustify LARS. The plug-in approach replaces the classical correlations in LARS by robust correlation estimates. The cleaning approach first transforms the data set by shrinking the outliers toward the bulk of the data (which we call multivariate Winsorization) and then applies LARS to the transformed data. We show that the plug in approach is time-efficient and scalable and that the bootstrap can be used to stabilize its results. We recommend using bootstrapped robustified LARS to sequence a number of candidate predictors to form a reduced set from which a more refined model can be selected.
Keywords:
bootstrap
computational complexity
robust prediction
stepwise algorithm
Winsorization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
J
IF:
3
Papers:
5.2K
Citations:
4.8W
Organization
View more organizations
Cited Papers
.png){kind=icon}
ShareSave