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The Resource Asymptotic Nonparametric Statistical Analysis of Stationary Time Series, by Daniil Ryabko, (electronic resource)
Asymptotic Nonparametric Statistical Analysis of Stationary Time Series, by Daniil Ryabko, (electronic resource)
Resource Information
The item Asymptotic Nonparametric Statistical Analysis of Stationary Time Series, by Daniil Ryabko, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Boston University Libraries.This item is available to borrow from all library branches.
Resource Information
The item Asymptotic Nonparametric Statistical Analysis of Stationary Time Series, by Daniil Ryabko, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Boston University Libraries.
This item is available to borrow from all library branches.
 Summary
 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. 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 socalled 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
 Language
 eng
 Extent
 VIII, 77 p. 4 illus.
 Contents

 1 Introduction
 2 Preliminaries
 3 Basic inference
 Clustering and changepoint problems
 5 Hypothesis Testing
 6 Generalizations
 References
 Isbn
 9783030125646
 Label
 Asymptotic Nonparametric Statistical Analysis of Stationary Time Series
 Title
 Asymptotic Nonparametric Statistical Analysis of Stationary Time Series
 Statement of responsibility
 by Daniil Ryabko
 Language
 eng
 Summary
 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. 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 socalled 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
 http://library.link/vocab/creatorName
 Ryabko, Daniil
 http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsaut
 CYkaT3a9gSQ
 Image bit depth
 0
 LC call number
 Q334342
 Literary form
 non fiction
 http://library.link/vocab/relatedWorkOrContributorName
 SpringerLink
 Series statement
 SpringerBriefs in Computer Science,
 http://library.link/vocab/subjectName

 Artificial intelligence
 Coding theory
 Artificial Intelligence
 Coding and Information Theory
 Label
 Asymptotic Nonparametric Statistical Analysis of Stationary Time Series, by Daniil Ryabko, (electronic resource)
 Antecedent source
 mixed
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Color
 not applicable
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 Contents
 1 Introduction  2 Preliminaries  3 Basic inference  Clustering and changepoint problems  5 Hypothesis Testing  6 Generalizations  References
 Dimensions
 unknown
 Extent
 VIII, 77 p. 4 illus.
 File format
 multiple file formats
 Form of item
 electronic
 Isbn
 9783030125646
 Level of compression
 uncompressed
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Other control number
 10.1007/9783030125646
 Other physical details
 online resource.
 Quality assurance targets
 absent
 Reformatting quality
 access
 Specific material designation
 remote
 System control number
 (DEHe213)9783030125646
 Label
 Asymptotic Nonparametric Statistical Analysis of Stationary Time Series, by Daniil Ryabko, (electronic resource)
 Antecedent source
 mixed
 Carrier category
 online resource
 Carrier category code
 cr
 Carrier MARC source
 rdacarrier
 Color
 not applicable
 Content category
 text
 Content type code
 txt
 Content type MARC source
 rdacontent
 Contents
 1 Introduction  2 Preliminaries  3 Basic inference  Clustering and changepoint problems  5 Hypothesis Testing  6 Generalizations  References
 Dimensions
 unknown
 Extent
 VIII, 77 p. 4 illus.
 File format
 multiple file formats
 Form of item
 electronic
 Isbn
 9783030125646
 Level of compression
 uncompressed
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code
 c
 Other control number
 10.1007/9783030125646
 Other physical details
 online resource.
 Quality assurance targets
 absent
 Reformatting quality
 access
 Specific material designation
 remote
 System control number
 (DEHe213)9783030125646
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