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Time Series Analysis by State Space Methods (Oxford Statistical Science Series) by James Durbin, Siem Jan Koopman

Time Series Analysis by State Space Methods (Oxford Statistical Science Series) James Durbin, Siem Jan Koopman ebook
Format: djvu
ISBN: 0198523548, 9780198523543
Page: 273
Publisher: Oxford University Press

We have measured and analyzed balance data of 136 participants (young, n = 45; elderly, n = 91) comprising in all 1085 trials, and calculated the Sample Entropy (SampEn) for medio-lateral (M/L) and anterior-posterior (A/P) Center of Pressure (COP) together .. Instantaneous model results can be displayed in an animation screen for immediate review and time series results can be written to an external file for further analysis. Oxford Statistical Science Series. Thus, we estimate how the non- linearity . Between good and bad fits is a continuum of so-so, the place where most simulation-observation (S-O) fits in the social sciences are found (see any issue of the Journal of Artificial Societies and Social Simulation). Inspired by Time Series and Systems Analysis with Applications. Durbin J, Koopman SJ: Time series analysis by state space methods, of. Quantifies the nonlinearity of the time series by comparing nonlinear-prediction errors with an optimum linear- prediction error using the statistical inference of the cross- validation (CV) method [4]. In such a case, nonuniform embedding [7–9] reduces the problem of interference between the linear and nonlinear models, because the nonuniform embedding accurately re- constructs an attractor in a state space. New York: Oxford University Press; 2001. The Hurst parameter H (after the hydrologist Harold Hurst) is related to a scaling property of time series x(t) and is also though of as one of the metrics for complexity (for which there is no universal definition [33]). 2.1: Ordinal Pattern Analysis (OPA) is a collection of statistical methods for measuring the extent to which the ordinal properties of a set of predictions match the ordinal properties of a set of observations.