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Statistical methods for modeling human dynamics : an interdisciplinary dialogue

Author: Sy-Miin Chow; Emilio Ferrer; Fushing Hsieh
Publisher: New York : Routledge, ©2010.
Series: Notre Dame series on quantitative methodologies.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
First Published in 2010. Routledge is an imprint of Taylor & Francis, an informa company.
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Statistical methods for modeling human dynamics.
New York : Routledge, ©2010
(DLC) 2009042651
(OCoLC)458737715
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Sy-Miin Chow; Emilio Ferrer; Fushing Hsieh
ISBN: 9780203864746 0203864743
OCLC Number: 721907179
Description: 1 online resource (xvi, 428 pages) : illustrations.
Contents: Cover; Statistical Methods for Modeling Human Dynamics: An Interdisciplinary Dialogouse; Copyright; Contents; Preface; Acknowledgments; Chapter 1. Introduction and Section Overview; 1.1 Part I: Parametric and Exploratory Approaches for ExtractingWithin-Person Nonstationarities; 1.2 Part II: Representing and Extracting Intraindividual Change; 1.3 Part III:Modeling Interindividual Differences in Change and Interpersonal Dynamics; References; Part I: Parametric and Exploratory Approaches for Extracting Within-Person Nonstationarities. Chapter 2. Dynamic Modeling and Optimal Control of Intraindividual Variation: A Computational Paradigm for Nonergodic Psychological Processes2.1 Introduction; 2.2 Ergodicity; 2.3 (Lack of)Homogeneity; 2.4 Nonstationarity; 2.5 Illustrative EKFIS Application to a Nonstationary Time Series; 2.6 AMonteCarlo Study; 2.7 Optimal Control; 2.8 Conclusion; References; Chapter 3. Dynamic Spectral Analysis of Biomedical Signals with Application to Electroencephalogram and Heart Rate Variability; 3.1 Introduction; 3.2 Biomedical Signals; 3.3 Time-Frequency Representations. 3.4 Parametric Time-Varying Spectrum Estimation3.5 Case Study I: Estimation of ERS of EEG; 3.6 Case Study II: Estimation of HRV Dynamics During an Orthostatic Test; 3.7 Discussion; Acknowledgments; References; Chapter 4. Cluster Analysis for Nonstationary Time Series; 4.1 Introduction; 4.2 Fourier Analysis; 4.3 The WP Transform; 4.4 Clustering Nonstationary Time Series; 4.5 Simulations; 4.6 Illustrative Example; 4.7 Summary; Acknowledgments; Appendix 4.1: Estimation of the Posterior Probability in Equation 4.4; Appendix 4.2: BBA for Selecting the Best Clustering Basis. Appendix 4.3: Model-Based Feature Selection AlgorithmReferences; Chapter 5. Characterizing Latent Structure in Brain Signals; 5.1 Introduction; 5.2 Inferring Latent Structure via AR and TVA RModels; 5.3 Detecting Fatigue from EEGS: Experimental Setting and Data Analysis; 5.4 Conclusions and Future Directions; Acknowledgments; Appendix 5.1: Posterior Estimation in NDLMS; References; Chapter 6. A Closer Look at Two Approaches for Analysis and Classification of Nonstationary Time Series; Part II: Representing and Extracting Intraindividual Change.
Series Title: Notre Dame series on quantitative methodologies.
Responsibility: edited by Sy-Miin Chow, Emilio Ferrer, Fushing Hsieh.

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Features contributions from researchers in the fields of psychology, neuroscience, statistics, computer science, and physics. This title uses advanced techniques and applications to analyze data  Read more...

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"This is a timely and important book that addresses an integrated set of topics that will be of great interest to a broad audience of researchers studying human dynamics. The contributors are among Read more...

 
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