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Time series and wavelet analysisFestschrift in honor of Pedro A. Morettin /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Time series and wavelet analysisedited by Chang Chiann, Aluisio de Souza Pinheiro, Clélia Maria Castro Toloi.
Reminder of title:	Festschrift in honor of Pedro A. Morettin /
other author:	Chiann, Chang.
Published:	Cham :Springer Nature Switzerland :2024.
Description:	xxi, 293 p. :ill. (chiefly color), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Time-series analysis.
Online resource:	https://doi.org/10.1007/978-3-031-66398-7
ISBN:	9783031663987$q(electronic bk.)

Time series and wavelet analysisFestschrift in honor of Pedro A. Morettin /
Time series and wavelet analysisFestschrift in honor of Pedro A. Morettin /[electronic resource] :edited by Chang Chiann, Aluisio de Souza Pinheiro, Clélia Maria Castro Toloi. - Cham :Springer Nature Switzerland :2024. - xxi, 293 p. :ill. (chiefly color), digital ;24 cm.

- Part I Time Series and Econometrics -- Analysis of High-Frequency Seasonal Time Series -- Stochastic Volatility With Feedback -- Structural Breaks and Common Factors -- A Note About Calibration Tests for VaR and ES -- Dynamic Ordering Learning in Multivariate Forecasting -- A Generalization of the Ornstein-Uhlenbeck Process: Theoretical Results, Simulations and Estimation -- Does the Private Database Help to Explain Brazilian Inflation? -- Identifiability and Whittle Estimation of Periodic ARMA Models -- Dynamic Factor Copulas for Minimum-CVaR Portfolio Optimization -- Part II Wavelets -- Does White Noise Dream of Square Waves?: A Matching Pursuit Conundrum -- Robust Wavelet-based Assessment of Scaling with Applications -- An Overview of Spectral Graph Wavelets -- Statistical Inferences on Brain Functional Networks Using Graph Theory and Multivariate Wavestrapping: An fNIRS Hyperscanning Illustration -- UtilizingWavelet Transform in the Analysis of Scaling Dynamics for Milk Quality Evaluation -- Wavelet Estimation of Nonstationary Spatial Covariance Function.

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.

ISBN: 9783031663987$q(electronic bk.)

Standard No.: 10.1007/978-3-031-66398-7doiSubjects--Topical Terms:

181890
Time-series analysis.

LC Class. No.: QA280

Dewey Class. No.: 519.55

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505 0 $a - Part I Time Series and Econometrics -- Analysis of High-Frequency Seasonal Time Series -- Stochastic Volatility With Feedback -- Structural Breaks and Common Factors -- A Note About Calibration Tests for VaR and ES -- Dynamic Ordering Learning in Multivariate Forecasting -- A Generalization of the Ornstein-Uhlenbeck Process: Theoretical Results, Simulations and Estimation -- Does the Private Database Help to Explain Brazilian Inflation? -- Identifiability and Whittle Estimation of Periodic ARMA Models -- Dynamic Factor Copulas for Minimum-CVaR Portfolio Optimization -- Part II Wavelets -- Does White Noise Dream of Square Waves?: A Matching Pursuit Conundrum -- Robust Wavelet-based Assessment of Scaling with Applications -- An Overview of Spectral Graph Wavelets -- Statistical Inferences on Brain Functional Networks Using Graph Theory and Multivariate Wavestrapping: An fNIRS Hyperscanning Illustration -- UtilizingWavelet Transform in the Analysis of Scaling Dynamics for Milk Quality Evaluation -- Wavelet Estimation of Nonstationary Spatial Covariance Function.
520 $a 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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