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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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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 23 June 2021

Digital Filters

Digital Filters

Chapter:
1 (p.1) (p.2) (p.3) Digital Filters
Source:
Computational Statistics in Climatology
Author(s):

Ilya Polyak

Publisher:
Oxford University Press
DOI:10.1093/oso/9780195099997.003.0003

In this chapter, several systems of digital filters are presented. The first system consists of regressive smoothing filters, which are a direct consequence of the least squares polynomial approximation to equally spaced observations. Descriptions of some particular univariate cases of these filters have been published and applied (see, for example, Anderson, 1971; Berezin and Zhidkov, 1965; Kendall and Stuart, 1963; Lanczos, 1956), but the study presented in this chapter is more general, more elaborate in detail, and more fully illustrated. It gives exhaustive information about classical smoothing, differentiating, one- and two-dimensional filtering schemes with their representation in the spaces of time, lags, and frequencies. The results are presented in the form of algorithms, which can be directly used for software development as well as for theoretical analysis of their accuracy in the design of an experiment. The second system consists of harmonic filters, which are a direct consequence of a Fourier approximation of the observations. These filters are widely used in the spectral and correlation analysis of time series. The foundation for developing regressive filters is the least squares polynomial approximation (of equally spaced observations), a principal notion that will be considered briefly.

Keywords:   Bernoulli polynomials, Fourier coefficients, Gauss-Markov Theory, Student statistic, Tukey filter, derivative estimation, frequency characteristics, ground truth problem, harmonic filter, inverse normal matrix

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