Locally weighted least squares kernel regression and statistical evaluation of LIDAR measurements

Summary, in English

The LIDAR technique is an efficient tool in monitoring the distribution of atmospheric species of importance. We study the concentration of atmospheric atomic mercury in an Italian geothermal field and discuss the possibility of using recent results from local polynomial kernel regression theory for the evaluation of the derivative of the DIAL curve. A MISE-optimal bandwidth selector, which takes account of the heteroscedasticity in the regression is suggested. Further, we estimate the integrated amount of mercury in a certain area.

• Författare: Ulla Holst; Ola Hössjer; Claes Björklund; Pär Ragnarson; Hans Edner
• Avdelning/ar: Matematisk statistik; Atomfysik
• Publiceringsår: 1996
• Språk: Engelska
• Sidor: 401-416
• Publikation/Tidskrift/Serie: Environmetrics
• Volym: 7
• Issue: 4
• Fulltext: - 702 kB; Download statistics
• Länkar: http://www3.interscience.wiley.com/cgi-bin/fulltext/25021/PDFSTART; http://dx.doi.org/10.1002/(SICI)1099-095X(199607)7:4<401::AID-ENV221>3.0.CO;2-D
• Dokumenttyp: Artikel i tidskrift
• Förlag: John Wiley & Sons Inc.
• Ämne: Atom and Molecular Physics and Optics; Probability Theory and Statistics
• Nyckelord: LIDAR measurements; Locally weighted least squares regression; air pollution; atmospheric atomic mercury; geothermal field
• Aktiv: Published
• ISBN/ISSN/Övrigt: ISSN: 1099-095X