Confidence intervals and regions for the lasso by using stochastic variational inequality techniques in optimization
Confidence intervals and regions for the lasso by using stochastic variational inequality techniques in optimization
复制标题
通过在优化中使用随机变分不等式技术来确定套索的置信区间和区域
DOI：
--
复制
发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Kai Zhang
中科院分区：
文献类型：
--
作者：
Shu Lu;Yufeng Liu;Liang Yin;Kai Zhang
稀疏回归技术近年来很受欢迎，因为它们能够处理具有内置变量选择的高维数据。套索也许是最著名的例子之一。尽管在这个方向上进行了大量的工作，如何为稀疏正则化方法提供有效的推理仍然是一个具有挑战性的统计问题。我们采取一个独特的观点，这个问题，并建议利用随机变分不等式技术在优化中得到的置信区间和区域的套索。得到了该过程的一些理论性质。仿真和真实的数据的例子来证明该方法的性能。
Sparse regression techniques have been popular in recent years because of their ability in handling high dimensional data with built‐in variable selection. The lasso is perhaps one of the most well‐known examples. Despite intensive work in this direction, how to provide valid inference for sparse regularized methods remains a challenging statistical problem. We take a unique point of view of this problem and propose to make use of stochastic variational inequality techniques in optimization to derive confidence intervals and regions for the lasso. Some theoretical properties of the procedure are obtained. Both simulated and real data examples are used to demonstrate the performance of the method.
影响因子：
4.5
作者：
Lockhart R;Taylor J;Tibshirani RJ;Tibshirani R
通讯作者：
Tibshirani R