Scholarlink

<p>The authors consider the issue of hypothesis testing in varying-coefficient regression models with high-dimensional data. Utilizing kernel smoothing techniques, the authors propose a locally concerned U-statistic method to assess the overall significance of the coefficients. The authors establish that the proposed test is asymptotically normal under both the null hypothesis and local alternatives. Based on the locally concerned U-statistic, the authors further develop a globally concerned U-statistic to test whether the coefficient function is zero. A stochastic perturbation method is employed to approximate the distribution of the globally concerned test statistic. Monte Carlo simulations demonstrate the validity of the proposed test in finite samples.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Test for Varying-Coefficient Models with High-Dimensional Data

  • 期刊论文

  • 发表日期：

    2026年2月5日

查看全文
  • Lin Yang,
  • Yuzhao Gao,
  • Lianqiang Qu

摘要

The authors consider the issue of hypothesis testing in varying-coefficient regression models with high-dimensional data. Utilizing kernel smoothing techniques, the authors propose a locally concerned U-statistic method to assess the overall significance of the coefficients. The authors establish that the proposed test is asymptotically normal under both the null hypothesis and local alternatives. Based on the locally concerned U-statistic, the authors further develop a globally concerned U-statistic to test whether the coefficient function is zero. A stochastic perturbation method is employed to approximate the distribution of the globally concerned test statistic. Monte Carlo simulations demonstrate the validity of the proposed test in finite samples.