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Computational Statistics in Climatology$
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Ilya Polyak

Print publication date: 1996

Print ISBN-13: 9780195099997

Published to Oxford Scholarship Online: November 2020

DOI: 10.1093/oso/9780195099997.001.0001

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Averaging and Simple Models

Averaging and Simple Models

Chapter:
2 (p.61) Averaging and Simple Models
Source:
Computational Statistics in Climatology
Author(s):

Ilya Polyak

Publisher:
Oxford University Press
DOI:10.1093/oso/9780195099997.003.0004

Simple linear procedures for processing correlated observations are considered and interpreted in this chapter. Primarily, they present different schemes for averaging data. These procedures are important because climatology has historically dealt with spatial and temporal averaging of statistically dependent meteorological observations. The accuracy of such averaging is determined by the volume of data arid by its correlation structure. The examples presented in this chapter illustrate the level of accuracy that can be achieved within the framework of some assumptions about such correlation structure. Let us consider the principal relationships of the least squares method for the statistically dependent observations (see Rao, 1973). The basic assumptions are as follows.

Keywords:   autodispersion function, correlated observations, dispersion function, finite differences, instrumental variable, least squares method, mean estimator, normal equations, point estimates, signal ratio

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