期刊
STATISTICAL MODELLING
卷 17, 期 1-2, 页码 59-85出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/1471082X16681875
关键词
Bayesian modeling; Functional data analysis; functional regression; functional mixed models; linear mixed models
资金
- NIH [CA-107304, CA-016672]
- NSF [1550088]
- Direct For Biological Sciences
- Div Of Biological Infrastructure [1550088] Funding Source: National Science Foundation
In their article, Greven and Scheipl describe an impressively general framework for performing functional regression that builds upon the generalized additive modelling framework. Over the past number of years, my collaborators and I have also been developing a general framework for functional regression, functional mixed models, which shares many similarities with this framework, but has many differences as well. In this discussion, I compare and contrast these two frameworks, to hopefully illuminate characteristics of each, highlighting their respective strengths and weaknesses, and providing recommendations regarding the settings in which each approach might be preferable.
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