Discussion contribution "Functional models for time-varying random objects'' by Dubey and Müller (to appear in JRSS-B)

2019/11/19 by Wicher Bergsma, Bergsma, Wicher
Mathematics · Engineering · #Advanced Statistical Methods and Models #Statistical Methods and Inference #Control Systems and Identification

paper · pdf · doi:10.48550/arxiv.1911.08468

Abstract

In an inspiring paper Dubey and Müller (DM) extend PCA to the case that observations are metric-valued functions. As an alternative, we develop a kernel PCA approach, which we show is closely related to the DM approach. While kernel principal components (kPCs) are simply defined, DM require added complexity in the form of "object FPCs'' and "Fréchet scores".

Related

• Discussion of "Functional Models for Time-Varying Random Objects'' by Dubey and Müller
• Functional Models for Time-Varying Random Objects
• Dynamic Functional Principal Component
• Semi-Supervised Kernel PCA
• Extended Principal Component Analysis