对指数功率组合回归模型的模型选择
Yunlu Jiang1, Jiangchuan Liu1, Hang Zou1
1Department of Statistics and Data Science, College of Economics, Jinan University, Guangzhou 510632, China.
本研究引入了有限混合线性回归 (FMLR) 模型的新方法,以同时选择变量并确定组件数. 该方法使用指数级功率误差分布,优于现有方法,棒球工资数据上的BIC值较小.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
背景情况:
- 有限混合线性回归 (FMLR) 模型对于分析异质数据至关重要.
- 现有的方法可能无法有效地处理同时确定组件数和选择变量.
研究的目的:
- 为FMLR模型开发一种新的程序,同时确定组件数量并执行变量选择.
- 使用指数级功率误差分布,包括正常和拉普拉斯分布,以提高模型灵活性.
主要方法:
- 为FMLR模型引入了一种新的程序,其中包含了指数级功率误差分布.
- 在正规性条件下建立了对顺序和变量选择的理论一致性.
- 对非零参数估计器进行了研究的非对称正常性.
- 建议高效修改的预期最大化 (EM) 和最大化最大化 (MM) 算法进行优化.
主要成果:
- 提出的方法在顺序和变量选择方面都表现出一致性.
- 对于参数估计器来说,建立了非对称的正常性.
- 数字模拟证实了该方法的有限样本性能.
- 对棒球薪资数据的应用与现有方法相比,产生了较小的贝叶斯信息标准 (BIC) 值.
结论:
- 开发的程序有效地解决了在FMLR模型中同时确定组件数和选择变量的问题.
- 使用指数级功率误差分布为模拟异质数据提供了一个灵活的框架.
- 拟议的算法为统计问题提供了有效的实现.
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