01615nas a2200157   4500008004100000245008100041210006900122260000900191300001200200490000600212520105900218653002301277100001901300700002301319856011501342       2014                        eng d00aWavelet Methods in Interpolation of High-Frequency Spatial-Temporal Pressure0 aWavelet Methods in Interpolation of HighFrequency SpatialTempora  c2014  a52–680 v83 aThe location-scale and whitening properties of wavelets make them more favorable for interpolating high-frequency monitoring data than Fourier-based methods. In the past, wavelets have been used to simplify the dependence structure in multiple time or spatial series, but little has been done to apply wavelets as a modeling tool in a space–time setting, or, in particular, to take advantage of the localization of wavelets to capture the local dynamic characteristics of high-frequency meteorological data. This paper analyzes minute-by-minute atmospheric pressure data from the Atmospheric Radiation Measurement program using different wavelet coefficient structures at different scales and incorporating spatial structure into the model. This approach of modeling space–time processes using wavelets produces accurate point predictions with low uncertainty estimates, and also enables interpolation of available data from sparse monitoring stations to a high density grid and production of meteorological maps on large spatial and temporal scales.10aBusiness Analytics1 aChang, Xiaohui1 aStein, Michael, L.  u/biblio/wavelet-methods-interpolation-high-frequency-spatial-temporal-pressure01375nas a2200157   4500008004100000245008800041210006900129260000900198300001400207490000700221520079900228653002301027100001901050700002301069856012501092       2013                        eng d00aDecorrelation Property of Discrete Wavelet Transform Under Fixed-Domain Asymptotics0 aDecorrelation Property of Discrete Wavelet Transform Under Fixed  c2013  a8001-80130 v593 aTheoretical aspects of the decorrelation property of the discrete wavelet transform when applied to stochastic processes have been studied exclusively from the increasing-domain perspective, in which the distance between neighboring observations stays roughly constant as the number of observations increases. To understand the underlying data-generating process and to obtain good interpolations, fixed-domain asymptotics, in which the number of observations increases in a fixed region, is often more appropriate than increasing-domain asymptotics. In the fixed-domain setting, we prove that, for a general class of inhomogeneous covariance functions, with suitable choice of wavelet filters, the wavelet transform of a nonstationary process has mostly asymptotically uncorrelated components.10aBusiness Analytics1 aChang, Xiaohui1 aStein, Michael, L.  u/biblio/decorrelation-property-discrete-wavelet-transform-under-fixed-domain-asymptotics