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Published in:
Big Data Analysis with RMT Read chapter Read first chapter

2023 | OriginalPaper | Chapter

4. The RMT-Tests

Authors : Mieko Tanaka-Yamawaki, Yumihiko Ikura

Published in: Principal Component Analysis and Randomness Test for Big Data Analysis

Publisher: Springer Nature Singapore

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Abstract

We created RMT-test as a new tool to measure the randomness of sufficiently long data strings and present the results of applying it to various situations. The goal of RMT-test is to find good labels for a given data string. Good labels can greatly reduce the burden of big data analysis, especially for huge financial data.

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previous chapter RMT-PCA for the Stock Markets
next chapter Applications of the RMT-Test
Literature
Tanaka-Yamawaki M, Yang X, Itoi R (2012b) Moment approach for quantitative evaluation of randomness based on RMT formula. In: Intelligent Decision Technologies, vol 16. SIST(Springer), New York, pp 423–432
Knuth DE (1997) The art of computer programming: seminumerical algorithms, vol 2, 3rd edn. Addison-Wesley, Reading
Matsumoto M, Nishimura T (1998) Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. Model. Comput. Simul. 8:3–30CrossRef
Sengupta AM, Mitra PP (1999) Distribution of singular values for some random matrices. Phys Rev E 60:3389–3392CrossRef
Marchenko VA, Pastur LA (1967) Distribution of eigenvalues for some sets of random matrices. Mathematics of the USSR-Sbornik 1:457–483CrossRef
Yang X, Mikamori Y, Tanaka-Yamawaki M (2013a) Predicting the security levels of stock investment by using the RMT-test. Prog Theor Phys Suppl 22:1172–1181
Metadata
Title
The RMT-Tests
Authors
Mieko Tanaka-Yamawaki
Yumihiko Ikura
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Principal Component Analysis and Randomness Test for Big Data Analysis

Print ISBN: 978-981-19-3966-2

Electronic ISBN: 978-981-19-3967-9

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-19-3967-9_4

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