Smoothing spatial data by estimating mean local variance
By: Laura D. Johnson
Contributors: Naval Postgraduate School (U.S.). Dept. of Operations Research
Publisher: Naval Postgraduate School; Available from National Technical Information Service
Published: 1988
Place of publication: Monterey, Calif; Springfield, Va
Language: eng
Number of pages: 24
Subjects: NONPARAMETRIC STATISTICS; REGRESSION  ANALYSIS; AIR POLLUTION
Other titles: NPS-55-88-005.
Description: A nearest neighbor nonparametric regression method is used to estimate air pollution levels      at other than measured points. The method requires an appropriate smoother.      Cross-validation is used to determine the appropriate smoother. An alternative method is      introduced to determine an appropriate level of smoothing which involves minimizing mean      local variance. Mean local variance is a function of the size of a circular window. It is      minimized for two pollutants in Ohio, New York and Florida. The smoother obtained by      cross- validation using Ohio's data is compared to that obtained by minimizing mean local      variance. Keywords: Air quality; Sulfur dioxide; Suspended particulates; Statistical data;      Pollution concentrations.
Notes: Title from cover.

"NPS-55-88-005."

"April 1988."

AD A195 302.

Includes bibliographical references (p. 23-24).

aq/aq cc:9116 11/19/98