Titlebook: Asymptotic Nonparametric Statistical Analysis of Stationary Time Series; Daniil Ryabko Book 2019 The Author(s), under exclusive license to

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Clustering and Change-Point Problems,of all the processes in question may be the same. For the change-point problem, the main focus of this chapter is on the case of a single change; on overview of the more general formulations is given, with the corresponding results presented without proofs.

Omnipotent

The Einstein-Podolsky-Rosen problem,of all the processes in question may be the same. For the change-point problem, the main focus of this chapter is on the case of a single change; on overview of the more general formulations is given, with the corresponding results presented without proofs.

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2191-5768 ming stationarity alone. While for most of the problem for which  a solution is found this solution is algorithmically realizable, the main objective in this ar978-3-030-12563-9978-3-030-12564-6Series ISSN 2191-5768 Series E-ISSN 2191-5776

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Basic Inference,n how to construct consistent estimates of a distance between stationary ergodic process distributions. As an easy application of this construction, an algorithm for solving the so-called three-sample problem for this class of processes is presented. On the other hand, it is demonstrated that there

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Clustering and Change-Point Problems,same distributions, while change-point problems are concerned with delimiting parts of a sample that are generated by a different process distributions. Building on the results of the previous chapter, here we are trying to solve these more general problems avoiding the need to answer the “same-diff

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Hypothesis Testing,l formulation encompasses a variety of problems, including model verification, such as testing that a process is Markov versus it is stationarity ergodic but not Markov, and property testing, such as testing for independence or homogeneity. This chapter is concerned with the general problem of chara