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Spectral‐based non‐central F mixed effect models, with application to otoacoustic emissions
Author(s) -
Wei Lai,
Craigmile Peter F.,
King Wayne M.
Publication year - 2012
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.2012.00789.x
Subject(s) - audiogram , replicate , mathematics , otoacoustic emission , gaussian , noise (video) , audiology , narrowband , distortion (music) , spectral shape analysis , tone (literature) , white noise , statistics , covariate , acoustics , computer science , spectral line , hearing loss , medicine , artificial intelligence , telecommunications , amplifier , computer network , physics , bandwidth (computing) , quantum mechanics , astronomy , image (mathematics) , art , literature
In an experimental design involving replicate time series, on a number of experimental units, we consider the statistical problem of modelling the signal‐to‐noise ratio (SNR) of a number of sinusoidal features of interest, observed in the presence of nuisance sinusoids and non‐white Gaussian errors. Based on local spectral F statistics, we introduce non‐central F mixed effect models to assess and characterize the variability in the SNRs over units and experimental conditions. We apply these non‐central F mixed models to the analysis of distortion product otoacoustic emissions (DPOAEs), retrograde sinusoidal pressure variations produced in the nonlinear cochlea by two‐tone stimulation. Due to the narrowband nature of both the evoking stimuli and the emission, DPOAEs potentially represent a non‐behavioural analogue of the pure‐tone audiogram. However, substantial inter‐ and intra‐subject variability currently limits their diagnostic validity. We model the cubic distortion product, the strongest such DPOAE, in a sample of 15 normal‐hearing subjects. Our results demonstrate the ability to detect established gender‐ and evoking stimuli‐dependent features, while being able to characterize the inter‐ and intra‐subject variability. A demonstration that these methods can be readily applied to healthy patient populations indicates their utility in studying clinical populations.