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Analysis of lidar fields using local polynomial regression

Lindström, Torgny LU ; Holst, Ulla LU and Weibring, P (2005) In Environmetrics 16(6). p.619-634
Abstract
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... (More)
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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
spatial dependence, non-parametric, local bandwidth selection, air pollution, heteroscedastic observations, variance-function estimation
in
Environmetrics
volume
16
issue
6
pages
619 - 634
publisher
John Wiley & Sons
external identifiers
  • wos:000231784000005
  • scopus:24944468239
ISSN
1099-095X
DOI
10.1002/env.726
language
English
LU publication?
yes
id
63f80159-36c5-4c3b-a039-ec8a3859a7d3 (old id 224994)
date added to LUP
2007-08-03 13:28:28
date last changed
2017-07-02 03:36:04
@article{63f80159-36c5-4c3b-a039-ec8a3859a7d3,
  abstract     = {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.},
  author       = {Lindström, Torgny and Holst, Ulla and Weibring, P},
  issn         = {1099-095X},
  keyword      = {spatial dependence,non-parametric,local bandwidth selection,air pollution,heteroscedastic observations,variance-function estimation},
  language     = {eng},
  number       = {6},
  pages        = {619--634},
  publisher    = {John Wiley & Sons},
  series       = {Environmetrics},
  title        = {Analysis of lidar fields using local polynomial regression},
  url          = {http://dx.doi.org/10.1002/env.726},
  volume       = {16},
  year         = {2005},
}