Time Series and Wavelet Analysis
Springer International Publishing (Verlag)
978-3-031-66397-0 (ISBN)
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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.
Chang Chiann has a BSc (1989) and an MSc (1993) in Statistics from the University of São Paulo, São Paulo, Brazil, where she also received a PhD (1997) and a Postdoctoral degree (2000), under the supervision of Prof. Pedro A. Morettin. From 1997 to 2000 she joined the International Cooperation Project (CNPq/NSF Agreement) with Professor David R. Brillinger, from the University of California, Berkeley. She is currently Associate Professor 3 at the University of São Paulo. Her research mainly focuses on Wavelets, Time Series, Bioavailability and Bioequivalence Analysis, Biosimilars, and Clinical Trials.
Aluísio de Souza Pinheiro received a BSc in Statistics from the National School of Statistical Sciences, Rio de Janeiro, Brazil, in 1989, an MSc in Statistics from the University of Campinas, Campinas, Brazil, in 1992, and a PhD in statistics from the University of North Carolina (UNC) at Chapel Hill, Chapel Hill, NC, USA, in 1997. After his PhD, he joined the Department of Statistics, University of Campinas. In 2012, he was the P. K. Sen Distinguished Visiting Professor of Biostatistics with the UNC Gillings School of Global Public Health. He is currently a Professor of Statistics at the University of Campinas, Campinas, Brazil. His research interests include nonparametric statistics, especially on generalized U-statistics and wavelet methodologies with applications in high-dimensional data, genetics, education, image analysis and the biological sciences. Dr. Pinheiro was on the Governing Board of the Brazilian Statistical Association on two occasions, as General Secretary (2010-2012) and as Treasurer (2018-2020).
Clélia Maria Castro Toloi has a master's (1980) and a PhD (1988) from the University of São Paulo, supervised by Prof. Pedro A. Morettin. She is currently a retired Associate Professor 3 at the University of São Paulo. She has experience in the area of Probability and Statistics, with emphasis on Spectral Analysis and Time Series Models. Dr. Toloi was President of the Brazilian Statistical Association (2002-2004).
- 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.
| Erscheint lt. Verlag | 4.2.2025 |
|---|---|
| Zusatzinfo | X, 240 p. 77 illus., 66 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik |
| Wirtschaft | |
| Schlagworte | Copulas • Dynamic Models • Forecasting • Graph Statistical Models • high-dimensional data • identifiability • reduction of dimensionality • spatial models • Spectral Analysis • Time Series • Volatility Models • Wavelets |
| ISBN-10 | 3-031-66397-7 / 3031663977 |
| ISBN-13 | 978-3-031-66397-0 / 9783031663970 |
| Zustand | Neuware |
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