Principal Component Analysis and Randomness Tests for Big Data Analysis
Author:   Mieko Tanaka-Yamawaki ,  Yumihiko Ikura
Publisher:   Springer Verlag, Japan
Edition:   1st ed. 2022
Volume:   25
ISBN:  

9784431559047


Publication Date:   11 September 2022
Format:   Hardback
Availability:   Not yet available   Availability explained
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Principal Component Analysis and Randomness Tests for Big Data Analysis


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Author:   Mieko Tanaka-Yamawaki ,  Yumihiko Ikura
Publisher:   Springer Verlag, Japan
Imprint:   Springer Verlag, Japan
Edition:   1st ed. 2022
Volume:   25
ISBN:  

9784431559047


ISBN 10:   4431559043
Publication Date:   11 September 2022
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Forthcoming
Availability:   Not yet available   Availability explained
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.

Table of Contents

Chapter 1 Introduction.- Chapter 2 Big Data Analysis by Using Rectangular-Shaped data: Mathematical Tools.- Chapter 3 Application to Extract Trendy Sectors in Stock Markets (RMT-PCA).- Chapter 4 Application to Measure Randomness of Time Series (RMT-test)- 4-1 Pseudo-Random Generators and Physical Random Generators- 4-2 Relation Between the Randomness of Tick-wise Prices and the Future Performance of Individual Stocks- 4-3 Other Application of the RMT-test.- Chapter 5 Human Random Generation and Its Applications- 5-1 Experimental Conditions- 5-2 Selection of Indices.- Chapter 6 Visualization by Means of Self-Organized Maps (SOM).- Chapter 7 Conclusion and Future Perspectives.

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Mieko Tanaka, Professor Graduate School of Engineering, Tottori University, Tottori, Japan

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