Analysis of lidar fields using local polynomial regression
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/224994
- author
- Lindström, Torgny LU ; Holst, Ulla LU and Weibring, P
- organization
- publishing date
- 2005
- 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 Inc.
- 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
- 2016-04-01 12:00:44
- date last changed
- 2022-03-20 22:12:01
@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}},
keywords = {{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 Inc.}},
series = {{Environmetrics}},
title = {{Analysis of lidar fields using local polynomial regression}},
url = {{http://dx.doi.org/10.1002/env.726}},
doi = {{10.1002/env.726}},
volume = {{16}},
year = {{2005}},
}