Clustering Nonstationary Circadian Rhythms using Locally Stationary Wavelet Representations

DOI10.1137/16M1108078zbMath1462.62665arXiv1607.08827OpenAlexW2963635859MaRDI QIDQ4643796

Rachael J. Oakenfull, Seth J. Davis, Jessica K. Hargreaves, Marina I. Knight, Jonathan William Pitchford

Publication date: 29 May 2018

Published in: Multiscale Modeling & Simulation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1607.08827

zbMATH Keywords

unsupervised learningnonstationary processesplant circadian clockcircadian oscillationsevolutionary wavelet spectrumnondecimated wavelet transform

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nontrigonometric harmonic analysis involving wavelets and other special systems (42C40) Biological rhythms and synchronization (92B25)

Related Items (5)

Wavelet testing for a replicate-effect within an ordered multiple-trial experiment ⋮ Trend locally stationary wavelet processes ⋮ The local partial autocorrelation function and some applications ⋮ Wavelet spectral testing: application to nonstationary circadian rhythms ⋮ Multiscale spectral modelling for nonstationary time series within an ordered multiple-trial experiment

Uses Software

• Silhouettes
• fda (R)
• wavethresh
• Wavos
• clusfind
• waveclock

Cites Work

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