.png){kind=icon}
返回
POST-SELECTION INFERENCE VIA ALGORITHMIC STABILITY
.png){kind=icon}
OA
AI
DOI：10.1214/23-AOS2303.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
When the target of statistical inference is chosen in a data-driven manner, the guarantees provided by classical theories vanish. We propose a solution to the problem of inference after selection by building on the framework of algorithmic stability, in particular its branch with origins in the field of differential privacy. Stability is achieved via randomization of selection and it serves as a quantitative measure that is sufficient to obtain nontrivial post-selection corrections for classical confidence intervals. Importantly, the underpinnings of algorithmic stability translate directly into computational efficiency-our method computes simple corrections for selective inference without recourse to Markov chain Monte Carlo sampling.
Keyword:
Post-selection inference
selective inference
stability
differential privacy
model selection
linear regression
期刊
IF:
3.7
论文数:
2.8K
被引数:
2.9W
机构
引用论文
.png){kind=icon}
On the Length of Post-Model-Selection Confidence Intervals Conditional on Polyhedral Constraints关于以多面体约束为条件的后模型选择置信区间的长度OAAI
OAAI分享收藏Preparation and Characterization of Inclusion Complexes of Poly(propylene glycol) with Cyclodextrins
PREAI
分享收藏
查看更多内容