Tree-structured wavelet estimation in a mixed effects model for spectra of replicated time series

Jean Marc Freyermuth*, Hernando Ombao, Rainer Von Sachs

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

This article develops a method for estimating the spectrum of a stationary process using time series traces recorded from experimental designs. Our procedure estimates the "common" log-spectrum and the variability over the traces (or subjects) using a mixed effects model. We combine spatially adaptive smoothing methods with recursive dyadic partitioning to construct a model for predicting subject-specific effects. The method is easy to implement and can handle large datasets because it uses the discrete wavelet transform which is computationally efficient. Numerical studies confirm that the proposed method performs very well despite its simplicity. The method is also applied to a multisubject electroencephalogram dataset.

Original languageEnglish (US)
Pages (from-to)634-646
Number of pages13
JournalJournal of the American Statistical Association
Volume105
Issue number490
DOIs
StatePublished - Jun 1 2010

Keywords

  • Log-spectrum estimation
  • Mixed effects models
  • Panel time series
  • Recursive dyadic partitioning
  • Tree-structured wavelets

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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