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Название: Asymptotic Nonparametric Statistical Analysis of Stationary Time Series Автор: Daniil Ryabko
Издательство: Springer
Серия: SpringerBriefs in Computer Science
Год: 2019
Язык: английский
Формат: pdf (true), mobi, epub
Размер: 10.16 MB
Stationarity is a very general, qualitative assumption, that can be assessed on the basis of application specifics. It is thus a rather attractive assumption to base statistical analysis on, especially for problems for which less general qualitative assumptions, such as independence or finite memory, clearly fail. However, it has long been considered too general to be able to make statistical inference. One of the reasons for this is that rates of convergence, even of frequencies to the mean, are not available under this assumption alone. Recently, it has been shown that, while some natural and simple problems, such as homogeneity, are indeed provably impossible to solve if one only assumes that the data is stationary (or stationary ergodic), many others can be solved with rather simple and intuitive algorithms. The latter include clustering and change point estimation among others. In this volume I summarize these results. The emphasis is on asymptotic consistency, since this the strongest property one can obtain assuming stationarity alone.
Название: Machine Learning for Asset Managers