对于低和高维设置中的非线性半参数回归模型的后收缩策略
S Ejaz Ahmed1, Dursun Aydın2, Ersin Yılmaz2
1Brock University, Faculty of Mathematics and Science, Brock University, St. Catharines, ON, L2S 3A1, USA.
The international journal of biostatistics
|November 19, 2025
概括
本研究引入了使用修改的高斯-牛顿法对非线性半参数回归模型 (NSRM) 的新型收缩估计器. 这些方法有效地处理稀疏,高维的数据,对乳腺癌研究有希望.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 非线性半参数回归模型 (NSRM) 是复杂的.
- 稀疏性和高维数据给估计带来了挑战.
- 现有的惩罚式最小方程方法不适合非线性参数组件.
研究的目的:
- 在稀缺性下开发NSRM的半参数估计策略.
- 为低维和高维数据适应高斯-牛顿法.
- 在NSRM的参数组件中解决非线性结构的挑战.
主要方法:
- 修改了用于NSRM的高斯-牛顿方法.
- 低维 (强/稀) 和高维 (强/弱/稀) 数据的系数分区.
- 对于高维情景的加权坡方法.
- 发展收缩估计器的发展.
主要成果:
- 对于低维和高维的案例,得出了理论上的非对称结果.
- 广泛的模拟研究证明了估计器的性能.
- 应用到乳腺癌数据集 (BCUS和威斯康星州) 验证了实用的实用性.
结论:
- 建议的收缩估计器对于稀疏的NSRM是有效的.
- 修改后的高斯-牛顿方法成功处理非线性参数组件.
- 这些发现支持这些估计器对乳腺癌研究和生物统计学的相关性.
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