在线循环回归模型中的变量选择
Onur Camli1, Zeynep Kalaylioglu1, Ashis SenGupta2
1Department of Statistics, Middle East Technical University, Ankara, Türkiye.
Journal of applied statistics
|November 16, 2023
概括
本研究介绍了一种强大的贝叶斯拉索方法,用于线性循环回归模型中的变量选择. 新的经验贝叶斯方法提高了系数估计的稳定性和模型复杂性的降低.
科学领域:
- 统计 统计 统计 统计
- 数据科学数据科学数据科学
背景情况:
- 循环回归模型在气象学,生物学和地质学中广泛使用.
- 变量选择仍然是循环回归分析中的一个重大挑战.
研究的目的:
- 在线循环回归模型中解决变量选择问题.
- 开发一个强大的贝叶斯方法,以改善推理和模型复杂性降低.
主要方法:
- 研究了线性循环回归中的标准贝叶斯拉索的局限性.
- 建议使用实证贝叶斯 (EB) 类型的方法来强化贝叶斯的拉索.
- 实现了Gibbs采样,用于调整参数的超前构造.
主要成果:
- 标准贝叶斯拉索对超前设置表现出敏感性,导致推断不稳定.
- 拟议的基于EB的超前构建提供了计算可行性.
- 模拟研究证实,EB-Gibbs采样 (EB-GS) 超前导致更强大的推断.
结论:
- 开发的强化贝叶斯拉索为线性循环回归中的变量选择提供了一种有效的方法.
- 经验贝叶斯方法提高了系数估计的稳定性和可靠性.
- 这种方法有效地减少了模型的复杂性,同时保持了强大的统计推理.
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