非高斯非有界误差回归模型中 RKHS 岭分组稀疏估计量的风险上界
统计理论
2020-09-25 v1 其他统计学
统计理论
摘要
我们考虑估计具有非高斯且非有界误差的未知回归模型的元模型问题。该元模型属于一个再生核希尔伯特空间，其构造为希尔伯特空间的直接和，从而得到包含变量及其相互作用的加性分解。该元模型的估计量通过最小化带惩罚的经验最小二乘准则得到，惩罚项为希尔伯特范数与经验 -范数之和。在此背景下，我们建立了估计量的经验 风险与 风险的上界。
引用
@article{arxiv.2009.11646,
title = {Risk upper bounds for RKHS ridge group sparse estimator in the regression model with non-Gaussian and non-bounded error},
author = {Halaleh Kamari and Sylvie Huet and Marie-Luce Taupin},
journal= {arXiv preprint arXiv:2009.11646},
year = {2020}
}
备注
Previously this appeared as arXiv:1905.13695v3 which was submitted as a replacement by accident. arXiv admin note: text overlap with arXiv:1701.04671