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Analysis of lidar fields using local polynomial regression

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Author

Summary, in English

Lidar (light detection and ranging) is a laser based tool for remote measurement of several atmospheric species of importance. We consider the analysis of a field, consisting of several consecutive measurements, in which the concentrations are proportional to the derivatives in the directions of the light paths. Inference is based on local polynomial kernel regression, both for estimation of the derivatives of the mean-function and for estimation of the variance-function. Bivariate bandwidth matrices are selected using the empirical-bias bandwidth selector (EBBS) adapted to allow for dependent data and to support selection of bivariate bandwidths. The estimation procedure is demonstrated on measurements of atomic mercury from the Solvay industries mercury cell chlor-alkali plant in Rosignano Solvay, Italy.

Publishing year

2005

Language

English

Pages

619-634

Publication/Series

Environmetrics

Volume

16

Issue

6

Document type

Journal article

Publisher

John Wiley & Sons Inc.

Topic

  • Probability Theory and Statistics

Keywords

  • spatial dependence
  • non-parametric
  • local bandwidth selection
  • air pollution
  • heteroscedastic observations
  • variance-function estimation

Status

Published

ISBN/ISSN/Other

  • ISSN: 1099-095X