Tohru Ozaki

Time Series Modeling of Neuroscience Data (eBook, PDF)

• Format: PDF

Produktbeschreibung

Due to recent advances in methodology that offer significant improvements over conventional methods, there is increasing interest in the use of time series models for the study of neuroscience data such as EEG, MEG, fMRI, and NIRS. This book presents an overview of time series models for the study of neuroscience data. Accessible to applied statisticians as well as quantitatively trained neuroscientists, the book is supported by many real examples to illustrate the methods and provides useful instructions for computational problems, enabling readers to develop their own computational toolbox to apply the methods to real data.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 574
• Erscheinungstermin: 26. Januar 2012
• Englisch
• ISBN-13: 9781420094619
• Artikelnr.: 38457966

Autorenporträt

Tohru Ozaki is a mathematician and statistician. He received his BSc in mathematics from the University of Tokyo in 1969. He then joined the Institute of Statistical Mathematics (ISM), Tokyo, in 1970 and study and worked with Hirotugu Akaike. He received his DSc from Tokyo Institute of Technology in 1981 under the supervision of Akaike. From 1987 to 2008, he was a professor at ISM and, after Akaike's retirement, served as the director of the prediction and control group. His major research areas include time series analysis, nonlinear stochastic dynamic modeling, predictive control, signal processing and their applications in neurosciences, control engineering, and financial engineering.

While he was at ISM, Ozaki was engaged in various projects in applied time series analysis in science and engineering: EEG dynamic inverse problems, spatial time series modeling of fMRI data, causality analysis in behavioral science, modeling nonlinear dynamics in ship engineering, predictive control design in fossil power plant control, seasonal adjustment in official statistics, heteroscedastic modeling and risk-sensitive control in financial engineering, nonlinear dynamic modeling in macroeconomics, spectral analysis of seismology data, point process modeling of earthquake occurrence data, river-flow prediction in stochastic hydrology, etc.

Ozaki retired from ISM in 2008. Since then he has been a visiting professor at Tohoku University, Sendai, Japan, and at Queensland University of Technology, Brisbane, Australia. He has been involved in supporting several research projects (in dynamic modeling of neuroscience data, fossil power plant control design, and risk-sensitive control in financial engineering) in universities and industry. He has also led, through his international research network, a time series research group called Akaike Innovation School from his office in Mount Fuji and organizes seminars every summer.

Inhaltsangabe

Introduction. Dynamic Models for Time Series Prediction: Time Series
Prediction and the Power Spectrum. Discrete-Time Dynamic Models.
Multivariate Dynamic Models. Continuous-Time Dynamic Models. Some More
Models. Related Theories and Tools: Prediction and Doob Decomposition.
Dynamics and Stationary Distributions. Bridge between Continuous-Time
Models and Discrete-Time Models. Likelihood of Dynamic Models. State Space
Modeling: Inference Problem (a) for State Space Models. Inference Problem
(b) for State Space Models. Art of Likelihood Maximization. Causality
Analysis. The New and Old Problems. References. Index.