The wavelet variance is a scale-based decomposition of the process
variance for a time series that has been used, for example, to analyze
time deviations in atomic clocks, variations in soil properties in
agricultural plots, accumulation of snow fields in the polar regions and
marine atmospheric boundary layer turbulence. In this talk we will
provide a basic introduction to the ideas behind the wavelet variance and
will then discuss the statistical properties of its estimators based upon
a sampled time series.