高相关设计下广义线性模型估计的非渐近界
On non-asymptotic bounds for estimation in generalized linear models with highly correlated design
摘要：
研究了高维广义线性模型和带$\ell_1$惩罚的经验风险最小化问题。我们的目的是提供一个非平凡的说明，非渐近界的估计可以得到不依赖于链接技术和/或剥离设备。
英文摘要：
We study a high-dimensional generalized linear model and penalized empirical risk minimization with $\ell_1$ penalty. Our aim is to provide a non-trivial illustration that non-asymptotic bounds for the estimator can be obtained without relying on the chaining technique and/or the peeling device.
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。
