Efficient functional ANOVA through wavelet-domain Markov groves

TL;DR

This paper presents a Bayesian wavelet-domain fANOVA method using a Markov grove model that efficiently detects factor effects in functional data, outperforming existing methods in simulations and real data analysis.

Contribution

It introduces a novel wavelet-domain Bayesian fANOVA approach with a Markov grove model for joint factor effect modeling, enabling efficient computation without MCMC.

Findings

Outperforms existing wavelet-domain fANOVA methods in simulations

Provides exact posterior sampling without MCMC

Efficiently analyzes real orthosis data

Abstract

We introduce a wavelet-domain functional analysis of variance (fANOVA) method based on a Bayesian hierarchical model. The factor effects are modeled through a spike-and-slab mixture at each location-scale combination along with a normal-inverse-Gamma (NIG) conjugate setup for the coefficients and errors. A graphical model called the Markov grove (MG) is designed to jointly model the spike-and-slab statuses at all location-scale combinations, which incorporates the clustering of each factor effect in the wavelet-domain thereby allowing borrowing of strength across location and scale. The posterior of this NIG-MG model is analytically available through a pyramid algorithm of the same computational complexity as Mallat's pyramid algorithm for discrete wavelet transform, i.e., linear in both the number of observations and the number of locations. Posterior probabilities of factor contributions can also be computed through pyramid recursion, and exact samples from the posterior can be drawn without MCMC. We investigate the performance of our method through extensive simulation and show that it outperforms existing wavelet-domain fANOVA methods in a variety of common settings. We apply the method to analyzing the orthosis data.