Low-Rank Expectile Representations of a Data Matrix, with Application to Diurnal Heart Rates

TL;DR

This paper introduces a low-rank expectile matrix factorization framework that captures asymmetric tail behaviors in data, demonstrated on diurnal heart rate data, with robust structure recovery and insights into variability across subjects.

Contribution

It develops a novel low-rank expectile analysis method incorporating additive and multiplicative effects, suitable for incomplete data, and applies gradient descent for fitting.

Findings

Lower expectiles are more stable within subjects across days.

Upper expectiles show higher variability within subjects.

Simulation studies confirm accurate structure recovery.

Abstract

Low-rank matrix factorization is a powerful tool for understanding the structure of 2-way data, and is usually accomplished by minimizing a sum of squares criterion. Expectile analysis generalizes squared-error loss by introducing asymmetry, allowing tail behavior to be elicited. Here we present a framework for low-rank expectile analysis of a data matrix that incorporates both additive and multiplicative effects, utilizing expectile loss, and accommodating arbitrary patterns of missing data. The representation can be fit with gradient-descent. Simulation studies demonstrate the accuracy of the structure recovery. Using diurnal heart rate data indexed by person-days versus minutes within a day, we find divergent behavior for lower versus upper expectiles, with the lower expectiles being much more stable within subjects across days, while the upper expectiles are much more variable, even within subjects.