Wavelet Methods in Statistics with R

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A test for second‐order stationarity and approximate confidence intervals for localized autocovariances for locally stationary time series (2013, methodology): “The infant electrocardiogram data were originally analysed in Nason et al. (2000), section 4.2, and their first differences analysed in Nason (2008), section 5.3.7.”
An improved wavelet–ARIMA approach for forecasting metal prices (2014, neutral): “The MODWT contains all possible shifted versions of the DWT (Nason, 2008); it applies the same reduction of coefficients as the DWT in the scale dimension, but produces the maximum number of coefficients in the time dimension, so that u = k for the MODWT.”
SPLIDHOM: A Method for Homogenization of Daily Temperature Observations (2011, methodology): “…several techniques have been tested: kernel smoothers (Brockmann et al. 1993; too noisy at the edge for datascarcity reasons), wavelet thresholding (Nason 2008; too sensitive to small outliers), and locally weighted polynomial regression and scatterplot smoothing (LOESS; Cleveland and Grosse 1991;…”
Penalized Wavelets: Embedding Wavelets into Semiparametric Regression (2011, background): “In the R package wavethresh (Nason [48]) this is referenced using family="DaubExPhase" and the smoothness number is denoted by filter.number.”