A general approach to statistical modeling of physical laws : nonparametric regression
Grabec, Igor (Author)

URLURL - Presentation file, Visit http://arxiv.org/abs/0704.0089 This link opens in a new window

Statistical modeling of experimental physical laws is based on the probability density function of measured variables. It is expressed by experimental data via a kernel estimator. The kernel is determined objectively by the scattering of data during calibration of experimental setup.A physical law, which relates measured variables, is optimally extracted from experimental data by the conditional average estimator. It is derived directly from the kernel estimator and corresponds to a general nonparametric regression. The proposed method is demonstrated by the modeling of a return map of noisy chaotic data. In this example, the nonparametric regression is used to predict a future value of chaotic time series from the present one. The mean predictor error is used in the definition of predictor quality, while the redundancy is expressed by the mean square distance betweendata points. Both statistics are used in a new definition of predictor cost function. From the minimum of the predictor cost function, a proper number of data in the model is estimated.

Keywords:fizika, fizikalni zakoni, statistično modeliranje, neparametrične regresije, kernel estimator, experimental information, complexity, redundancy, modeling, physical law, nonparametric regression
Work type:Not categorized (r6)
Organization:FS - Faculty of Mechanical Engineering
Number of pages:17 f.
COBISS.SI-ID:10030875 Link is opened in a new window
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