Multivariate functional data present theoretical and practical complications which are not found in univariate functional data. One of these is a situation where the component functions of multivariate functional data are positive and are subject to mutual time warping. That is, the component processes exhibit a common shape but are subject to systematic phase variation across their domains in addition to subject-specific time warping, where each subject has its own internal clock. This motivates a novel model for multivariate functional data that connects such mutual time warping to a latent deformation-based framework by exploiting a novel time warping separability assumption. This separability assumption allows for meaningful interpretation and dimension reduction. The resulting Latent Deformation Model is shown to be well suited to represent commonly encountered functional vector data. The proposed approach combines a random amplitude factor for each component with population based registration across the components of a multivariate functional data vector and includes a latent population function, which corresponds to a common underlying trajectory. We propose estimators for all components of the model, enabling implementation of the proposed data-based representation for multivariate functional data and downstream analyses such as Fr\'echet regression. Rates of convergence are established when curves are fully observed or observed with measurement error. The usefulness of the model, interpretations, and practical aspects are illustrated in simulations and with application to multivariate human growth curves and multivariate environmental pollution data.
翻译:多变量功能数据呈现出理论和实际的复杂情况,而这些复杂情况在单轨功能数据中并不存在。其中之一是,多变量功能数据的组成部分功能是积极的,并会相互时间扭曲。即,组合过程呈现出一个共同的形状,但除了按主题进行时间扭曲外,各不同领域的系统阶段性变化,每个主体都有自己的内部时钟。这激励了多变量功能数据的新模式,将这种相互时间扭曲与潜在畸形框架连接起来,利用新的时间扭曲分离假设。这一分离假设可以进行有意义的解释和减少尺寸。由此产生的延迟变形模型显示非常适合代表常见的功能矢量数据。拟议方法将每个组成部分的随机变异因素与基于人口的登记结合起来,每个主体的功能有其自身的内部变异功能矢量,并包含一个与共同的基本轨迹相对的潜伏功能功能功能功能功能。我们建议模型的所有组成部分的估算,使多变量功能数据和下游分析能够实现有意义的解释和尺寸分析。在观察到的Fr\\+曲线时,所观测到的数据曲线的趋同性。</s>