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Capturing variation in functional data via Bayesian modeling

Published on:

19 July 2023

Primary Category:

Methodology

Paper Authors:

Jiarui Zhang,

Jiguo Cao,

Liangliang Wang

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Key Details

Proposes robust Bayesian FPCA using skew elliptical distributions

Handles asymmetry and outliers better than conventional methods

Models covariance function via spectral decomposition with priors

Extends to sparse functional data unlike previous Bayesian FPCA

Simulation studies demonstrate superior performance with outliers

AI generated summary

Capturing variation in functional data via Bayesian modeling

This paper develops a robust Bayesian approach for functional principal component analysis. It models observations using skew elliptical distributions to handle asymmetry and outliers. The method approximates the covariance function via spectral decomposition and places priors on parameters. It extends the model to sparse functional data. Simulation studies show the method outperforms others with outliers. Applications to environmental and biological data illustrate effectiveness in identifying abnormal curves.

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