相关实验视频
Updated: Jan 17, 2026

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
使用R包 PFLRR进行惩罚性功能回归
Rob Cameron1, Tianyu Guan2, Haolun Shi1
1Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, Canada.
处罚功能回归模型缩短了系数函数,预测因素影响到截止点的反应. R包PFLR为这些模型提供了先进的方法,通过模拟和真实世界的数据来证明.
科学领域:
- 统计 统计 统计 统计
- 功能数据分析 功能数据分析
背景情况:
- 函数回归模型的效果在于系数函数被截断.
- 截断系数函数发生在功能预测因素仅在特定时间点影响响应时.
- 处罚方法对于准确估计此类模型至关重要.
研究的目的:
- 引入R套件PFLR用于处罚功能回归.
- 提供一套用于估计具有截断系数函数的模型的方法.
- 通过模拟和现实世界的应用来证明软件包的实用性.
主要方法:
- 在PFLR包中实施了四种不同的处罚功能回归方法.
- 利用模拟来评估实施的方法的性能.
- 应用分析颗粒物排放数据的方法.
主要成果:
- 该PFLR包提供了用于高级功能回归的多功能工具.
- 模拟证实了用于截断系数函数的实施方法的有效性.
- 对颗粒物数据的应用表明了实际的实用性.
结论:
- 该PFLR包有效地解决了功能回归与截断系数函数的功能回归.
- 它提供了可靠的估计,可视化和解释方法.
- 该包是功能数据分析研究人员的宝贵资源.
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