期刊
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
卷 29, 期 3, 页码 620-628出版社
AMER STATISTICAL ASSOC
DOI: 10.1080/10618600.2020.1713797
关键词
B-spline basis functions; Functional data analysis; Functional linear regression; Group bridge approach; Locally sparse; Penalized B-splines
资金
- Natural Sciences and Engineering Research Council of Canada (NSERC)
We study a scalar-on-function truncated linear regression model which assumes that the functional predictor does not influence the response when the time passes a certain cutoff point. We approach this problem from the perspective of locally sparse modeling, where a function is locally sparse if it is zero on a substantial portion of its defining domain. In the truncated linear model, the slope function is exactly a locally sparse function that is zero beyond the cutoff time. A locally sparse estimate then gives rise to an estimate of the cutoff time. We propose a nested group bridge penalty that is able to specifically shrink the tail of a function. Combined with the B-spline basis expansion and penalized least squares, the nested group bridge approach can identify the cutoff time and produce a smooth estimate of the slope function simultaneously. The proposed nested group bridge estimator is shown to be consistent, while its numerical performance is illustrated by simulation studies. The proposed nested group bridge method is demonstrated with an application of determining the effect of the past engine acceleration on the current particulate matter emission. for this article are available online.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据