000116130 001__ 116130
000116130 005__ 20190316234119.0
000116130 037__ $$aREP_WORK
000116130 245__ $$aHierarchical wavelet modelling of environmental sensor data
000116130 269__ $$a2007
000116130 260__ $$c2007
000116130 336__ $$aWorking Papers
000116130 520__ $$aMotivated by the need to smooth and to summarize multiple simultaneous time series arising from networks of environmental monitors, we propose a hierarchical wavelet model for which estimation of hyperparameters can be performed by marginal maximum likelihood. The result is an empirical Bayes thresholding procedure whose results improve on those of wavethresh in terms of mean square error. We apply the approach to data from the SensorScope environmental modelling system, and briefly discuss issues that arise concerning variance estimation in this context.
000116130 6531_ $$aEmpirical Bayes
000116130 6531_ $$aEnvironmental sensor
000116130 6531_ $$aHierarchical model
000116130 6531_ $$aMixture model
000116130 6531_ $$aNormal distribution
000116130 6531_ $$aSensorScope
000116130 6531_ $$aSpike-and-slab model
000116130 6531_ $$aWavelet.
000116130 700__ $$aRuffieux, Yann
000116130 700__ $$g111184$$aDavison, A. C.$$0240476
000116130 8564_ $$uhttps://infoscience.epfl.ch/record/116130/files/yann.pdf$$zn/a$$s1730981
000116130 909C0 $$xU10124$$0252136$$pSTAT
000116130 909CO $$ooai:infoscience.tind.io:116130$$qGLOBAL_SET$$pworking$$pSB
000116130 937__ $$aSTAT-WORKINGPAPER-2008-001
000116130 973__ $$sPUBLISHED$$aEPFL
000116130 980__ $$aWORKING