Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Berke, Olaf | de |
dc.date.accessioned | 2004-12-06T18:38:58Z | - |
dc.date.available | 2004-12-06T18:38:58Z | - |
dc.date.issued | 1998 | de |
dc.identifier.uri | http://hdl.handle.net/2003/4881 | - |
dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-14542 | - |
dc.description.abstract | Noisy observations form the basis for almost every scientific research and especially in environmental monitoring. The Noise is often an effect of imprecise instruments which cause measurement errors. If the noise variance is known it is possible to filter out the contaminating noise from the observations and then to predict the latent signal process. Solutions for this problem exist for time series application and will be briefly reviewed. In the geostatistical literature, i.e. for the analysis of spatial data, similar methods have been foreshadowed in the literature and will be outlined in this work. | en |
dc.format.extent | 154635 bytes | - |
dc.format.extent | 164932 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.format.mimetype | application/postscript | - |
dc.language.iso | en | de |
dc.publisher | Universitätsbibliothek Dortmund | de |
dc.subject | geostatistics | en |
dc.subject | Kalman Filter | en |
dc.subject | kriging | en |
dc.subject | prediction | en |
dc.subject | signal | en |
dc.subject | time series analysis | en |
dc.subject.ddc | 310 | de |
dc.title | Filtering the Noise from Time Series and Spatial Data | en |
dc.type | Text | de |
dc.type.publicationtype | report | en |
dcterms.accessRights | open access | - |
Appears in Collections: | Sonderforschungsbereich (SFB) 475 |
Files in This Item:
File | Description | Size | Format | |
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98_18.pdf | DNB | 161.07 kB | Adobe PDF | View/Open |
tr18-98.ps | 151.01 kB | Postscript | View/Open |
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This item is protected by original copyright
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