From Multiple Gaussian Sequences to Functional Data and Beyond: A Stein Estimation Approach
从多组高斯序列到函数型数据：一种 Stein 估计方法
Summary We expand the notion of Gaussian sequence models to n experiments and propose a Stein estimation strategy which relies on pooling information across experiments. An oracle inequality is established to assess conditional risks given the underlying effects, based on which we can quantify the size of relative error and obtain a tuning-free recovery strategy that is easy to compute, produces model parsimony and extends to unknown variance. We show that the simultaneous…