%0 Journal Article
%T 一种基于最小二乘解的高维单指标模型半监督估计方法<br>A Semi-Supervised Estimation Method for High-Dimensional Single-Index Models Based on the Least Squares Solution
%A 史志恒
%A 崔文泉
%J Pure Mathematics
%P 311-324
%@ 2160-7605
%D 2025
%I Hans Publishing
%R 10.12677/PM.2025.155180
%X 本研究在协变量为椭圆分布的假设下，建立了最小二乘解与单指标系数在方向上的相合性。基于此相合性，本研究直接使用最小二乘解构造了指标系数的半监督估计量，从而克服了估计联系函数这一挑战。具体地，本研究使用无标签数据中蕴含的协变量分布信息辅助精度矩阵估计。同时针对厚尾分布情形引入了一种截断技术，拓宽了本方法的适用面。本文在不同尾部情形下分别建立了方法的理论收敛速度，理论表明无标签数据在一定条件下能够提升高维单指标系数的估计效果，并在渐近意义下达到minimax最优的收敛速率。<br> Under the assumption of elliptically distributed covariates, we establish the directional consistency between the least squares solution and the single-index coefficients. Based on this, we directly construct a semi-supervised estimator using the least squares solution. Specifically, the distributional information of covariates contained in the unlabeled data is utilized to assist in precision matrix estimation. Furthermore, for heavy-tailed distributions, a truncation technique is introduced, extending the applicability of the proposed method. Theoretical convergence rates are derived for scenarios with different tail properties, demonstrating that unlabeled data can enhance estimation performance. The results show that the method achieves the minimax optimal convergence rate asymptotically.
%K 高维单指标模型，半参数模型，半监督学习，线性回归，半监督线性模型<br> High-Dimensional Single-Index Models
%K Semiparametric Models
%K Semi-Supervised Learning
%K Linear Regression
%K Semi-Supervised Linear Models
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=116321