用p样条估计纵向研究中的导数曲线

IF 1.2 4区 数学 Q2 STATISTICS & PROBABILITY
María Alejandra Hernández, Dae-Jin Lee, María Xosé Rodríguez-álvarez, María Durbán
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引用次数: 0

摘要

曲线导数的估计是许多学科都感兴趣的问题。它允许提取重要的特征,从而深入了解底层流程。在纵向数据的背景下,导数允许描述个体的生物特征或发现感兴趣的变化区域。虽然有几种方法来估计特定学科的曲线及其导数,但由于这些时间过程过程的复杂性,仍然存在开放的问题。在这篇文章中,我们说明了使用p样条模型来估计纵向数据的导数。我们还提出了一种新的惩罚作用于群体和特定学科的水平,以解决导数估计中的欠平滑和边界问题。通过仿真对该方法的实际性能进行了评价,并与一种替代方法进行了比较。最后,本文提出了一项对125名青年职业学院足球运动员纵向身高测量的应用,其目标是分析他们随时间的成长和成熟模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Derivative curve estimation in longitudinal studies using P-splines
The estimation of curve derivatives is of interest in many disciplines. It allows the extraction of important characteristics to gain insight about the underlying process. In the context of longitudinal data, the derivative allows the description of biological features of the individuals or finding change regions of interest. Although there are several approaches to estimate subject-specific curves and their derivatives, there are still open problems due to the complicated nature of these time course processes. In this article, we illustrate the use of P-spline models to estimate derivatives in the context of longitudinal data. We also propose a new penalty acting at the population and the subject-specific levels to address under-smoothing and boundary problems in derivative estimation. The practical performance of the proposal is evaluated through simulations, and comparisons with an alternative method are reported. Finally, an application to longitudinal height measurements of 125 football players in a youth professional academy is presented, where the goal is to analyse their growth and maturity patterns over time.
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来源期刊
Statistical Modelling
Statistical Modelling 数学-统计学与概率论
CiteScore
2.20
自引率
0.00%
发文量
16
审稿时长
>12 weeks
期刊介绍: The primary aim of the journal is to publish original and high-quality articles that recognize statistical modelling as the general framework for the application of statistical ideas. Submissions must reflect important developments, extensions, and applications in statistical modelling. The journal also encourages submissions that describe scientifically interesting, complex or novel statistical modelling aspects from a wide diversity of disciplines, and submissions that embrace the diversity of applied statistical modelling.
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