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

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 f...

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Vydáno v:arXiv.org
Hlavní autoři: Ouyang, Shuge, Tang, Yunxuan, Benjamin Osafo Agyare
Médium: Paper
Jazyk:angličtina
Vydáno: Ithaca Cornell University Library, arXiv.org 06.12.2024
Témata:
Data analysis
Data recovery
Error analysis
Heart rate
Missing data
Representations
ISSN:2331-8422
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Shrnutí: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.
Bibliografie:content type line 50
SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
ISSN:2331-8422