Abstract
Data are available for the electroencephalograms (EEGs) of cattle before and after they received a mild electric shock. The purpose of the proposed statistical analysis is to extract from the data what differences there an in pre- and post-stun EEGs and possibly restrict attention to a small frequency band where there is a significant change. We study the log periodogram ratios for each animal and propose a stochastic model based method for smoothing these ratios. This method is novel in that it allows (i) for between animal variation, and (ii) the amount of local smoothing to adapt according to the data requirements. The smoothing method is implemented by utilizing a Kalman filter approach and the fixed interval smoothing algorithm, which allows us to obtain point-wise estimates and standard errors for the log periodogram ratio. Common animal effects which are intimated by animal-by-animal plots of log periodogram ratios against frequency are highlighted by this method.
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