Department of Statistics, The Ohio State University
Statistics and Biostatistics Colloquium Series

It is a standard practice in time series analysis to transform a time series to stationarity using, for example, detrending or differencing. This is an attractive idea because weakly stationary processes are characterized by only the mean and autocovariance function (or equivalently the spectral density function). This is an over-simplification, especially when the time-varying dependence is of key interest to the application. There is a growing need to be able to investigate, discriminate, and model multivariate time-varying structures, especially in areas such as hearing science, neurosciences, and atmospheric science. This talk will discuss the use of spectral and wavelet-based methods for examining nonstationary phenomena. We will apply our methodology to the analysis of distortion products, used in non-behavioural tests of hearing.

Meet the speaker in Room 212 Cockins Hall at 4:30 p.m. Refreshments will be served.