相关观测的稀疏性考虑
Sparsity considerations for dependent observations
摘要：
本文的目的是提供一个全面的介绍，在相依观测的背景下，L1-惩罚估计的研究。我们定义了一个一般的$\ell_{1}$ -惩罚估计，用于解决随机最优化问题。这个估计量是回归估计设置中的LASSO。最近的文献对LASSO的统计性能提供了强有力的理论保证，然而，他们通常只处理iid的情况。在这里，我们研究了各种依赖假设下的估计量。
英文摘要：
The aim of this paper is to provide a comprehensive introduction for the study of L1-penalized estimators in the context of dependent observations. We define a general $\ell_{1}$-penalized estimator for solving problems of stochastic optimization. This estimator turns out to be the LASSO in the regression estimation setting. Powerful theoretical guarantees on the statistical performances of the LASSO were provided in recent papers, however, they usually only deal with the iid case. Here, we study our estimator under various dependence assumptions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
