Download Chaos: A Statistical Perspective by Kung-Sik Chan, Howell Tong PDF

By Kung-Sik Chan, Howell Tong

It used to be none except Henri Poincare who on the flip of the final century, recognized that initial-value sensitivity is a primary resource of random­ ness. For statisticians operating in the conventional statistical framework, the duty of severely assimilating randomness generated via a simply de­ terministic method, known as chaos, is an highbrow problem. Like another statisticians, we've got taken up this problem and our interest as newshounds and contributors has led us to enquire past the sooner discoveries within the box. previous statistical paintings within the sector was once in general con­ cerned with the estimation of what's occasionally imprecisely referred to as the fractal measurement. in the course of the diverse phases of our writing, sizeable parts of the e-book have been utilized in lectures and seminars. those comprise the DMV (German Mathematical Society) Seminar software, the inaugural consultation of lectures to the quandary issues venture on the Peter Wall Institute of complex Stud­ ies, collage of British Columbia and the graduate classes on Time sequence research on the college of Iowa, the collage of Hong Kong, the Lon­ don university of Economics and Political technology, and the chinese language collage of Hong Kong. we have now for this reason benefitted tremendously from the reviews and proposals of those audiences in addition to from colleagues and neighbors. we're thankful to them for his or her contributions. Our specific thank you visit Colleen Cutler, Cees Diks, Barbel FinkensHidt, Cindy Greenwood, Masakazu Shi­ mada, Floris Takens and Qiwei Yao.

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Additional resources for Chaos: A Statistical Perspective

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It is then perhaps quite natural to try to retain the above Lyapunov exponent even after we have clothed the skeleton with dynamic noise. However, in the presence of dynamic noise, the problem arises as to how to quantify the divergence of two realisations starting from two nearby initial values. One way is to use a device analogous to the well-known coupling technique in probability theory by assuming that the two realisations share the same noise sequence. ) It seems that this device was first used by Herzel et al.

Again, this time series plot has a 'random' look. The complex pattern displayed by these two time series plots hints at the fact that these time series lose predictability quickly as the prediction horizon increases. -S. , Chaos: A Statistical Perspective © Springer Science+Business Media New York 2001 18 2. ,. ,. 1. 13 (first 100 transient values having been discarded). 01. The lower right diagram is the Q-Q plot of the two trajectories. Although these time series show irregular temporal patterns, they bear statistical resemblance.

The exponent p is defined as the correlation dimension of the attractor (or more correctly, the natural measure on the attractor). As an example, let the natural measure be U(O, 1), the uniform distribution on [0, I]. Then it can be checked that C(r) = 1- (1- r)2 = 2r - r2 '" r; Hence, the correlation dimension equals 1 for this case. To see this formula, first note that the joint distribution of X and X' is the uniform distribution on the square [0, I] x [0, I]. 4, which equals one minus the area of the two unshaded triangle corners.

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