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

Research output: Contribution to journalArticle

Abstract

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.

Details

Authors
  • Ulla Holst
  • Ola Hössjer
  • Claes Björklund
  • Pär Ragnarson
  • Hans Edner
Organisations
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • 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
Original languageEnglish
Pages (from-to)401-416
JournalEnvironmetrics
Volume7
Issue number4
Publication statusPublished - 1996
Publication categoryResearch
Peer-reviewedYes