Howell Tong – författare
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4 produkter
4 produkter
Inbunden, Engelska, 2001
1 090 kr
Skickas inom 10-15 vardagar
It was none other than Henri Poincare who at the turn of the last century, recognised that initial-value sensitivity is a fundamental source of random ness. For statisticians working within the traditional statistical framework, the task of critically assimilating randomness generated by a purely de terministic system, often known as chaos, is an intellectual challenge. Like some other statisticians, we have taken up this challenge and our curiosity as reporters and participants has led us to investigate beyond the earlier discoveries in the field. Earlier statistical work in the area was mostly con cerned with the estimation of what is sometimes imprecisely called the fractal dimension. During the different stages of our writing, substantial portions of the book were used in lectures and seminars. These include the DMV (German Mathematical Society) Seminar Program, the inaugural session of lectures to the Crisis Points Project at the Peter Wall Institute of Advanced Stud ies, University of British Columbia and the graduate courses on Time Series Analysis at the University of Iowa, the University of Hong Kong, the Lon don School of Economics and Political Science, and the Chinese University of Hong Kong. We have therefore benefitted greatly from the comments and suggestions of these audiences as well as from colleagues and friends. We are grateful to them for their contributions. Our special thanks go to Colleen Cutler, Cees Diks, Barbel FinkensHidt, Cindy Greenwood, Masakazu Shi mada, Floris Takens and Qiwei Yao.
Häftad, Engelska, 2010
1 091 kr
Skickas inom 10-15 vardagar
It was none other than Henri Poincare who at the turn of the last century, recognised that initial-value sensitivity is a fundamental source of random ness. For statisticians working within the traditional statistical framework, the task of critically assimilating randomness generated by a purely de terministic system, often known as chaos, is an intellectual challenge. Like some other statisticians, we have taken up this challenge and our curiosity as reporters and participants has led us to investigate beyond the earlier discoveries in the field. Earlier statistical work in the area was mostly con cerned with the estimation of what is sometimes imprecisely called the fractal dimension. During the different stages of our writing, substantial portions of the book were used in lectures and seminars. These include the DMV (German Mathematical Society) Seminar Program, the inaugural session of lectures to the Crisis Points Project at the Peter Wall Institute of Advanced Stud ies, University of British Columbia and the graduate courses on Time Series Analysis at the University of Iowa, the University of Hong Kong, the Lon don School of Economics and Political Science, and the Chinese University of Hong Kong. We have therefore benefitted greatly from the comments and suggestions of these audiences as well as from colleagues and friends. We are grateful to them for their contributions. Our special thanks go to Colleen Cutler, Cees Diks, Barbel FinkensHidt, Cindy Greenwood, Masakazu Shi mada, Floris Takens and Qiwei Yao.
E-bok
PDF, Engelska, 20131 367 kr
Läs direkt efter köp
It was none other than Henri Poincare who at the turn of the last century, recognised that initial-value sensitivity is a fundamental source of random ness. For statisticians working within the traditional statistical framework, the task of critically assimilating randomness generated by a purely de terministic system, often known as chaos, is an intellectual challenge. Like some other statisticians, we have taken up this challenge and our curiosity as reporters and participants has led us to investigate beyond the earlier discoveries in the field. Earlier statistical work in the area was mostly con cerned with the estimation of what is sometimes imprecisely called the fractal dimension. During the different stages of our writing, substantial portions of the book were used in lectures and seminars. These include the DMV (German Mathematical Society) Seminar Program, the inaugural session of lectures to the Crisis Points Project at the Peter Wall Institute of Advanced Stud ies, University of British Columbia and the graduate courses on Time Series Analysis at the University of Iowa, the University of Hong Kong, the Lon don School of Economics and Political Science, and the Chinese University of Hong Kong. We have therefore benefitted greatly from the comments and suggestions of these audiences as well as from colleagues and friends. We are grateful to them for their contributions. Our special thanks go to Colleen Cutler, Cees Diks, Barbel FinkensHidt, Cindy Greenwood, Masakazu Shi mada, Floris Takens and Qiwei Yao.
Del 2 - Nonlinear Time Series & Chaos
Chaos And Forecasting - Proceedings Of The Royal Society Discussion Meeting
Inbunden, Engelska, 1995
1 968 kr
Tillfälligt slut
It is now generally recognised that very simple dynamical systems can produce apparently random behaviour. In the last couple of years, attention has turned to focus on the flip side of this coin: random-looking time series (or random-looking patterns in space) may indeed be the result of very complicated processes or “real noise”, but they may equally well be produced by some very simple mechanism (a low-dimensional attractor). In either case, a long-term prediction will be possible only in probabilistic terms. However, in the very short term, random systems will still be unpredictable but low-dimensional chaotic ones may be predictable (appearances to the contrary). The Royal Society held a two-day discussion meeting on topics covering diverse fields, including biology, economics, geophysics, meteorology, statistics, epidemiology, earthquake science and many others. Each topic was covered by a leading expert in the field. The meeting dealt with different basic approaches to the problem of chaos and forecasting, and covered applications to nonlinear forecasting of both artificially-generated time series and real data from context in the above-mentioned diverse fields. This book marks a rather special and rare occasion on which prominent scientists from different areas converge on the same theme. It forms an informative introduction to the science of chaos, with special reference to real data.