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Published in: International Journal of Data Science and Analytics 4/2023

08-07-2023 | Regular Paper

Applications of the discrete-time Fourier transform to data analysis

Author: Dayne Sorvisto

Published in: International Journal of Data Science and Analytics | Issue 4/2023

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Abstract

We define a discrete analogue of the characteristic function for discrete random variable and develop numerical procedures for computing the discrete characteristic function for several well-known discrete random variables. We rigorously define what is meant by the Fourier transform of a probability mass function and also show how to reverse the process to recover the probability mass function of a discrete random variable, given a procedure for computing the characteristic function. Unlike previous work on the subject, our approach is novel in that we are not computing closed-form solutions of a continuous random variable but are focused on applying the methods to real-world data sets.

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Appendix
Available only for authorised users
Literature
1.
go back to reference Lukacs, E.: Characteristic Functions. Griffin, London (1970)MATH Lukacs, E.: Characteristic Functions. Griffin, London (1970)MATH
2.
go back to reference Pinsky, M.: Introduction to Fourier Analysis and Wavelets. Brooks/Cole. ISBN 978-0-534-37660-4 (2002) Pinsky, M.: Introduction to Fourier Analysis and Wavelets. Brooks/Cole. ISBN 978-0-534-37660-4 (2002)
Metadata
Title
Applications of the discrete-time Fourier transform to data analysis
Author
Dayne Sorvisto
Publication date
08-07-2023
Publisher
Springer International Publishing
Published in
International Journal of Data Science and Analytics / Issue 4/2023
Print ISSN: 2364-415X
Electronic ISSN: 2364-4168
DOI
https://doi.org/10.1007/s41060-023-00409-5

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