Téléchargez le livre : Asymptotic Nonparametric Statistical Analysis of Stationary Time Series

Asymptotic Nonparametric Statistical Analysis of Stationary Time Series

de

Éditeur :

Springer


Collection :

SpringerBriefs in Computer Science

Paru le : 2019-03-07

eBook Téléchargement , DRM LCP 🛈 DRM Adobe 🛈
Lecture en ligne (streaming)
52,74 €
Téléchargement immédiat
Dès validation de votre commande
Image Louise Reader présentation

Louise Reader

Lisez ce titre sur l'application Louise Reader.

badge apple Application Louise Reader
badge Android Application Louise Reader
Description
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 these  results are summarize.  The emphasis is on asymptotic consistency, since this the strongest property one can obtain assuming stationarity alone. While for most of the problem for which  a solution is found this solution is algorithmically realizable, the main objective in this area of research, the objective which is only partially attained, is to understand what is possible and what is not possible to do for stationary time series. The considered problems include homogeneity testing (the so-called two sample problem), clustering with respect to distribution, clustering with respect to independence, change point estimation, identity testing, and the general problem of composite hypotheses testing. For the latter problem, a topological criterion for the existence of a consistent test is presented.  In addition, a number of open problems is presented.
Pages
77 pages
Collection
SpringerBriefs in Computer Science
Parution
2019-03-07
Marque
Springer
EAN papier
9783030125639
EAN PDF
9783030125646
Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
7
Taille du fichier
1413 Ko
Prix
52,74 €
EAN EPUB
9783030125646
Informations sur l'ebook
Nombre pages copiables
0
Nombre pages imprimables
7
Taille du fichier
2488 Ko
Prix
52,74 €

Suggestions personnalisées