期刊
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
卷 26, 期 3, 页码 709-724出版社
AMER STATISTICAL ASSOC
DOI: 10.1080/10618600.2017.1279548
关键词
Generalized linear array models; Multidimensional smoothing; Penalized estimation; Proximal gradient algorithm
资金
- Danish Cancer Society
- Danish Strategic Research Council/Innovation Fund Denmark
- Villum Fonden [00013358] Funding Source: researchfish
Large-scale generalized linear array models (GLAMs) can be challenging to fit. Computation and storage of its tensor product design matrix can be impossible due to time and memory constraints, and previously considered design matrix free algorithms do not scale well with the dimension of the parameter vector. A new design matrix free algorithm is proposed for computing the penalized maximum likelihood estimate for GLAMs, which, in particular, handles nondifferentiable penalty functions. The proposed algorithm is implemented and available via the R package glamlasso. It combines several ideas-previously considered separately-to obtain sparse estimates while at the same time efficiently exploiting the GLAM structure. In this article, the convergence of the algorithm is treated and the performance of its implementation is investigated and compared to that of glmnet on simulated as well as real data. It is shown that the computation time for glamlasso scales favorably with the size of the problem when compared to glmnet. Supplementary materials, in the form of R code, data and visualizations of results, 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