000			04370cam a2200421 i 4500
005			20250919002744.0
008			150317s2012 flua bi 001 0 eng d
020			_a9781420094602 _q(hardcover) _cRM388.44
020			_a1420094602 _q(hardcover).
039		9	_a201506040923 _blan _c201506040922 _dlan _c201506021434 _dhaiyati _c201506021431 _dhaiyati _y03-17-2015 _zhamudah
040			_aDNLM/DLC _beng _cDLC _dYDX _dBTCTA _dBAKER _dYDXCP _dNLM _dUKMGB _dCDX _dNJI _dUKM _erda
090			_aRC386.6.O933
090			_aRC386.6 _b.O933
100	1		_aOzaki, Tohru, _d1944- _eauthor.
245	1	0	_aTime series modeling of neuroscience data / _cTohru Ozaki.
264		1	_aBoca Raton : _bTaylor & Francis, _c2012.
264		4	_a©2012.
300			_axxv, 548 pages : _billustrations ; _c25 cm.
336			_atext _2rdacontent
337			_aunmediated _2rdamedia
338			_avolume _2rdacarrier
490	1		_aChapman & Hall/CRC interdisciplinary statistics.
504			_aIncludes bibliographical references (p. 519-532) and index.
505	0		_aIntroduction -- Part I: 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 -- Part II: Related theories and tools -- Prediction and Doob decomposition -- Dynamics and stationary distributions -- Bridge between continuous-time models and discrete-time models -- Liklelihood of dynamic models -- Part III: State space modeling -- Inference problem (a) for state models -- Inference problem (b) for state space models -- Art of likelihood maximization -- Casuality analysis -- Conclusion : the new and old problems.
520			_a'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.
650		0	_aNervous system _xDiseases _xDiagnosis _xStatistical methods.
650		0	_aBrain mapping _xStatistical methods.
650		0	_aNeurosciences _xStatistics.
830		0	_aChapman & Hall/CRC interdisciplinary statistics.
907			_a.b16095911 _b2019-11-12 _c2019-11-12
942			_c01 _n0 _kRC386.6.O933
914			_avtls003581082
990			_anh
991			_aFakulti Sains dan Teknologi
998			_at _b2015-04-03 _cm _da _feng _gflu _y0 _z.b16095911
999			_c588662 _d588662