Rank dynamics for functional data
Rank dynamics for functional data
复制标题
DOI：
10.1016/j.csda.2020.106963
复制
发表时间：
2020-09-01
影响因子：
1.8
通讯作者：
Muller, Hans-Georg
中科院分区：
文献类型：
--
作者：
Chen, Yaqing;Dawson, Matthew;Muller, Hans-Georg
研究函数数据的横截面等级随时间的动态行为以及每个时间点观察到的曲线的等级及其时间演变可以为函数数据的时间动力学提供有价值的见解。这种方法在各种应用领域都很有价值。对于等级的动态分析，估计功能数据的横截面等级是第一步。提出了几种对排序函数数据感兴趣的统计量。为了量化等级随时间的演变，引入了一个等级导数模型，其中等级动态被分解为两个分量。一个组成部分对应于总体变化，另一个对应于个人变化，这两个变化都影响个人的排名轨迹。建立了这两个分量的适当估计的联合渐近正态分布。建议的方法通过模拟和三个纵向数据集进行了说明：从苏黎世纵向增长研究获得的增长曲线、美国1996至2015年的月度房价数据和2017赛季美国职业棒球大联盟的进攻数据。(C)2020爱思唯尔B.V.保留所有权利。
The study of the dynamic behavior of cross-sectional ranks over time for functional data and the ranks of the observed curves at each time point and their temporal evolution can yield valuable insights into the time dynamics of functional data. This approach is of interest in various application areas. For the analysis of the dynamics of ranks, estimation of the cross-sectional ranks of functional data is a first step. Several statistics of interest for ranked functional data are proposed. To quantify the evolution of ranks over time, a model for rank derivatives is introduced, where rank dynamics are decomposed into two components. One component corresponds to population changes and the other to individual changes that both affect the rank trajectories of individuals. The joint asymptotic normality for suitable estimates of these two components is established. The proposed approaches are illustrated with simulations and three longitudinal datasets: Growth curves obtained from the Zurich Longitudinal Growth Study, monthly house price data in the US from 1996 to 2015 and Major League Baseball offensive data for the 2017 season. (C) 2020 Elsevier B.V. All rights reserved.