The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

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

På svenska

Author

  • Ulla Holst
  • Ola Hössjer
  • Claes Björklund
  • Pär Ragnarson
  • Hans Edner

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.

Publishing year

1996

Language

English

Pages

401-416

Publication/Series

Environmetrics

Volume

7

Issue

4

Document type

Journal article

Publisher

John Wiley & Sons Inc.

Topic

  • Atom and Molecular Physics and Optics
  • Probability Theory and Statistics

Keywords

  • LIDAR measurements
  • Locally weighted least squares regression
  • air pollution
  • atmospheric atomic mercury
  • geothermal field

Status

Published

ISBN/ISSN/Other

  • ISSN: 1099-095X