Wavelet testing for a replicate-effect within an ordered multiple-trial experiment

Jonathan Embleton, Marina I. Knight, Hernando Ombao

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Experimental time series data collected across a sequence of ordered trials (replicates) often crop up in many fields, from neuroscience to circadian biology. In order to decide when to appropriately evade the simplifying assumption that all replicates stem from the same process, an assumption often untrue even when identical stimuli are applied, two novel tests are proposed that assess whether a significant trial-effect is manifest along the experiment. The modelling framework uses wavelet multiscale constructions that mitigate against the potential nonstationarities often present in experimental data, both across times and across replicates. The proposed tests are evaluated in thorough simulation studies and illustrated on neuroscience data, proving to be flexible tools with great promise in dealing with complex multiple-trials time series data and allowing the analyst to accordingly tune their subsequent analysis.
Original languageEnglish (US)
Pages (from-to)107456
JournalComputational Statistics & Data Analysis
DOIs
StatePublished - Feb 24 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-04-26
Acknowledgements: J. Embleton's research was supported by EPSRC studentship EP/N509802/1

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computational Mathematics
  • Applied Mathematics
  • Statistics and Probability

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