A two-component circular regression model for repeated measures auditory localization data
Version of Record online: 22 JAN 2013
Published 2013. This article is a US Government work and is in the public domain in the USA.
Journal of the Royal Statistical Society: Series C (Applied Statistics)
Volume 62, Issue 4, pages 515–534, August 2013
How to Cite
McMillan, G. P., Hanson, T. E., Saunders, G. and Gallun, F. J. (2013), A two-component circular regression model for repeated measures auditory localization data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 62: 515–534. doi: 10.1111/rssc.12004
- Issue online: 11 JUL 2013
- Version of Record online: 22 JAN 2013
- [Received May 2011. Final revision October 2012]
- Angular regression;
- Bimodal data;
- Sound localization;
- Symmetry models
Summary. Auditory localization experiments are conducted to evaluate human ability to locate the position of a source of sound, and to determine how population characteristics might affect this ability. These experiments generate data that are circular, bimodal and repeated, and have hypothesized symmetry patterns that should be included and tested within the modelling framework. We propose a two-part mixture of wrapped Cauchy densities for these bimodal angular data, with random effects to model correlation between repeated measures. The effects of signal position and types of symmetry in the signal response around the circle are modelled by using circular B-splines. The model is used to investigate the effects of age and hearing impairment on the ability to localize a low frequency signal.