# Functional principal component analysis estimator for non-Gaussian data

arXiv: https://arxiv.org/abs/2102.01286 · HTML: https://mlsanity.com/paper/2102.01286/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2021.

## Built on by (most cited first, 1 of 1)

- [Functional Autoencoder for Smoothing and Representation Learning](https://mlsanity.com/paper/2401.09499/) (2024)

## Abstract (arXiv)

Functional principal component analysis (FPCA) could become invalid when data involve non-Gaussian features. Therefore, we aim to develop a general FPCA method to adapt to such non-Gaussian cases. A Kenall's τ function, which possesses identical eigenfunctions as covariance function, is constructed. The particular formulation of Kendall's τ function makes it less insensitive to data distribution. We further apply it to the estimation of FPCA and study the corresponding asymptotic consistency. Moreover, the effectiveness of the proposed method is demonstrated through a comprehensive simulation study and an application to the physical activity data collected by a wearable accelerometer monitor.