Time series modeling of neuroscience data (Record no. 11157)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 03383cam a2200361Ia 4500 |
| 001 - CONTROL NUMBER | |
| control field | CRC00CE4602PDF |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | BD-DhSAU |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20151012144300.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS | |
| fixed length control field | m|||||o||d|||||||| |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr|||| |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 120126s2012 fluad sb 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781420094619 (ebook : PDF) |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | BD-DhSAU |
| Transcribing agency | BD-DhSAU |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (OCLC) | |
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) | RC341 |
| Local cutter number (OCLC) ; Book number/undivided call number, CALL (RLIN) | .O93 2012 |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) | |
| Classification number | 616.80475 |
| Item number | O992 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Ozaki, Tohru, |
| Dates associated with a name | 1944- |
| 245 10 - TITLE STATEMENT | |
| Title | Time series modeling of neuroscience data |
| Medium | [electronic resource] / |
| Statement of responsibility, etc. | Tohru Ozaki. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | Boca Raton : |
| Name of publisher, distributor, etc. | CRC Press, |
| Date of publication, distribution, etc. | 2012. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxv, 548 p. : |
| Other physical details | ill. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Chapman & Hall/CRC interdisciplinary statistics |
| 500 ## - GENERAL NOTE | |
| General note | "A Chapman & Hall book." |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc | Includes bibliographical references (p. 519-532) and index. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | pt. 1. Dynamic models for time series prediction -- pt. 2. Related theories and tools -- pt. 3. State space modeling. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | "Recent advances in brain science measurement technology have given researchers access to very large-scale time series data such as EEG/MEG data (20 to 100 dimensional) and fMRI (140,000 dimensional) data. To analyze such massive data, efficient computational and statistical methods are required. Time Series Modeling of Neuroscience Data shows how to efficiently analyze neuroscience data by the Wiener-Kalman-Akaike approach, in which dynamic models of all kinds, such as linear/nonlinear differential equation models and time series models, are used for whitening the temporally dependent time series in the framework of linear/nonlinear state space models. Using as little mathematics as possible, this book explores some of its basic concepts and their derivatives as useful tools for time series analysis. Unique features include: statistical identification method of highly nonlinear dynamical systems such as the Hodgkin-Huxley model, Lorenz chaos model, Zetterberg Model, and more Methods and applications for Dynamic Causality Analysis developed by Wiener, Granger, and Akaike state space modeling method for dynamicization of solutions for the Inverse Problems heteroscedastic state space modeling method for dynamic non-stationary signal decomposition for applications to signal detection problems in EEG data analysis An innovation-based method for the characterization of nonlinear and/or non-Gaussian time series An innovation-based method for spatial time series modeling for fMRI data analysis The main point of interest in this book is to show that the same data can be treated using both a dynamical system and time series approach so that the neural and physiological information can be extracted more efficiently. Of course, time series modeling is valid not only in neuroscience data analysis but also in many other sciences and engineering fields where the statistical inference from the observed time series data plays an important role"--Provided by publisher. |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE | |
| Additional physical form available note | Also available in print edition. |
| 538 ## - SYSTEM DETAILS NOTE | |
| System details note | Mode of access: World Wide Web. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Neurosciences. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Brain mapping. |
| 655 #7 - INDEX TERM--GENRE/FORM | |
| Genre/form data or focus term | Electronic books. |
| Source of term | lcsh |
| 776 1# - ADDITIONAL PHYSICAL FORM ENTRY | |
| International Standard Book Number | 9781420094602 (hardcover : alk. paper) |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| Uniform title | Interdisciplinary statistics. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://marc.crcnetbase.com/isbn/9781420094619">http://marc.crcnetbase.com/isbn/9781420094619</a> |
| Electronic format type | application/PDF |
No items available.