通过学习型曲线来确定预测模型的样本大小
Alimu Dayimu1, Nikola Simidjievski2,3, Nikolaos Demiris4
1Cambridge Clinical Trials Unit Cancer Theme, University of Cambridge, Cambridge, UK.
Statistics in medicine
|May 28, 2024
概括
本研究引入了学习曲线,以改善预测模型的样本大小计算. 跨样本大小借用信息可以提高预测模型的性能和稳定性.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 生物统计学 生物统计学
背景情况:
- 样本大小的确定对于预测模型的可靠性至关重要.
- 现有的方法往往缺乏稳定性和效率,尤其是在处理有限的数据或推断结果时.
研究的目的:
- 开发和评估用于预测建模中的样本大小确定新的方法.
- 通过利用学习曲线来提高样本大小计算的性能和统计效率.
主要方法:
- 提出两种方法:确定性学习曲线骨架和基于它的高斯过程模型.
- 利用各种学习算法进行初级终点建模和独特的疗效测量.
- 用二进制和生存终点说明方法.
主要成果:
- 通过学习曲线将单个样本大小计算结合起来,可以普遍提高性能.
- 基于高斯过程的学习曲线表现出卓越的稳定性和统计效率.
- 拟议的方法之间的计算效率是可比的.
结论:
- 学习曲线有效地整合了不同样本大小的信息,以更可靠地确定样本大小.
- 如果有,建议将样本大小推断与历史数据进行定.
- 高斯过程方法为预测建模中的样本大小规划提供了统计学上合理和高效的解决方案.
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