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Time Series And Wavelet Analysis Festschrift In Honour Of Pedro A Morettin 2024th Edition Chang Chiann

  • SKU: BELL-197415618
Time Series And Wavelet Analysis Festschrift In Honour Of Pedro A Morettin 2024th Edition Chang Chiann
$ 31.00 $ 45.00 (-31%)

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Time Series And Wavelet Analysis Festschrift In Honour Of Pedro A Morettin 2024th Edition Chang Chiann instant download after payment.

Publisher: Springer
File Extension: PDF
File size: 17.81 MB
Pages: 303
Author: Chang Chiann, Aluisio de Souza Pinheiro, Clélia Maria Castro Toloi
ISBN: 9783031663970, 9783031663987, 3031663977, 3031663985, B0DR26W8C6
Language: English
Year: 2024
Edition: 2024

Product desciption

Time Series And Wavelet Analysis Festschrift In Honour Of Pedro A Morettin 2024th Edition Chang Chiann by Chang Chiann, Aluisio De Souza Pinheiro, Clélia Maria Castro Toloi 9783031663970, 9783031663987, 3031663977, 3031663985, B0DR26W8C6 instant download after payment.

Prof. Pedro A. Morettin is a Distinguished Professor of Statistics at the Institute of Mathematics and Statistics of the University of São Paulo (IME-USP), where he has built an academic career spanning almost six decades. His work has had a significant impact on Time Series Analysis and Wavelet Statistical Methods, as exemplified by the papers appearing in this Festschrift, which are authored by renowned researchers in both fields. Besides his long-term commitment to research, Prof. Morettin is very active in mentoring and serving the profession. Moreover, he has written several textbooks, which are still a leading source of knowledge and learning for undergraduate and graduate students, practitioners, and researchers.

Divided into two parts, the Festschrift presents a collection of papers that illustrate Prof. Morettin’s broad contributions to Time Series and Econometrics, and to Wavelets. The reader will be able to learn state-of-the-art statistical methodologies, from periodic ARMA models, fractional Brownian motion, and generalized Ornstein-Uhlenbeck processes to spatial models, passing through complex structures designed for high-dimensional data analysis, such as graph and dynamic models. The topics and data features discussed here include high-frequency sampling, fNRIS, forecasting, portfolio apportionment, volatility assessment, dairy production, and inflation, which are relevant to econometrics, medicine, and the food industry. The volume ends with a discussion of several very powerful tools based on wavelets, spectral analysis, dimensionality reduction, self-similarity, scaling, copulas, and other notions.

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