功能数据分析:一个介绍和最近的发展
Jan Gertheiss1, David Rügamer2,3, Bernard X W Liew4
1Departmesnt of Mathematics and Statistics, School of Economics and Social Sciences, Helmut Schmidt University, Hamburg, Germany.
功能数据分析 (FDA) 提供了分析曲线和函数的工具,解决来自高维数据的挑战. 这篇论文介绍了FDA的关键方法和软件,用于医学,神经科学和化学领域的应用.
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:
- 功能数据分析 (FDA) 将统计方法扩展到曲线,图像和函数.
- 功能数据的高维性质带来了独特的分析挑战.
研究的目的:
- 介绍FDA的基本概念和技术.
- 描述软件实现和该领域的最新进展.
- 用实际例子展示FDA的方法.
主要方法:
- 描述性统计,异常值检测和功能数据的平滑.
- 功能主要组件分析 (FPCA) 和功能回归.
- 功能数据的分类,聚类和机器学习方法.
主要成果:
- 关于FDA核心技术的全面概述.
- 讨论振幅和相位变化,以及统计推断.
- 使用R软件对人类运动数据的方法的应用.
结论:
- 美国食品和药物管理局的方法广泛适用于医学和神经科学等科学学科.
- 该论文为实践学习提供了可访问的代码和数据.
- 稀缺的功能数据方法被突出显示为纵向数据分析.
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