For high-dimensional inference problems, statisticians have a number of competing interests. On the one hand, procedures should provide accurate estimation, reliable structure learning, and valid uncertainty quantification. On the other hand, procedures should be computationally efficient and able to scale to very high dimensions. In this note, I show that a very simple data-dependent measure can achieve all of these desirable properties simultaneously, along with some robustness to the error distribution, in sparse sequence models.

翻译：对于高维推论问题,统计人员有若干相互竞争的利益。一方面,程序应当提供准确的估算、可靠的结构学习以及有效的不确定性量化。另一方面,程序应当具有计算效率,并且能够达到非常高的尺寸。在本说明中,我表明,一个非常简单的数据依赖性计量方法可以同时实现所有这些可取的特性,同时以稀有的序列模型对误差分布进行某种稳健。