.png){kind=icon}
返回
Two-sample testing in high dimensions
.png){kind=icon}
PRE
AI
DOI：10.1111/rssb.12173.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We propose new methodology for two-sample testing in high dimensional models. The methodology provides a high dimensional analogue to the classical likelihood ratio test and is applicable to essentially any model class where sparse estimation is feasible. Sparse structure is used in the construction of the test statistic. In the general case, testing then involves non-nested model comparison, and we provide asymptotic results for the high dimensional setting. We put forward computationally efficient procedures based on data splitting, including a variant of the permutation test that exploits sparse structure. We illustrate the general approach in two-sample comparisons of high dimensional regression models (differential regression') and graphical models (differential network'), showing results on simulated data as well as data from two recent cancer studies.
Keyword:
Differential network
Differential regression
Gaussian graphical models
High dimensional regression
High dimensional two-sample testing
Non-nested hypotheses
Restricted log-likelihood-ratio statistic
Sparsity
期刊
J
IF:
3.6
论文数:
1.5K
被引数:
3.2W
机构
引用论文
Compression and Air Storage Systems for Small Size CAES Plants: Design and Off-design Analysis小型CAES工厂的压缩和空气存储系统: 设计和非设计分析
OAAI
OAAI分享收藏Two-Sample Covariance Matrix Testing and Support Recovery in High-Dimensional and Sparse Settings
PREAI
分享收藏
A TWO-SAMPLE TEST FOR HIGH-DIMENSIONAL DATA WITH APPLICATIONS TO GENE-SET TESTING.png)
ANNALS OF STATISTICSOAAI
ANNALS OF STATISTICSIF3.7
OAAI分享收藏Nonconcave penalized likelihood with a diverging number of parameters具有不同数量参数的非凹惩罚似然ANNALS OF STATISTICSOAAI
ANNALS OF STATISTICSIF3.7
OAAI分享收藏
查看更多内容