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LOCALLY WEIGHTED LEAST SQUARES KERNEL REGRESSION AND STATISTICAL EVALUATION OF LIDAR MEASUREMENTS
Author(s) -
HOLST ULLA,
HÖSSJER OLA,
BJÖRKLUND CLAES,
RAGNARSON PÄR,
EDNER HANS
Publication year - 1996
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/(sici)1099-095x(199607)7:4<401::aid-env221>3.0.co;2-d
Subject(s) - heteroscedasticity , polynomial regression , lidar , statistics , regression , regression analysis , mathematics , environmental science , linear regression , partial least squares regression , remote sensing , geography
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.