U-Statistics for Change under Alternatives
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TLDR
In this paper, the authors show that for all possible types of kernels, symmetric, antisymmetric, degenerate, non-degenerate, the test statistics are asymptotically normally distributed.About:
This article is published in Journal of Multivariate Analysis.The article was published on 2001-07-01 and is currently open access. It has received 36 citations till now. The article focuses on the topics: Estimator & Asymptotic distribution.read more
Citations
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Journal ArticleDOI
Extensions of some classical methods in change point analysis
Lajos Horváth,Gregory Rice +1 more
TL;DR: In this paper, the authors survey some classical results in change point analysis and recent extensions to time series, multivariate, panel and functional data, and present real data examples which illustrate the utility of the surveyed results.
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Change detection in autoregressive time series
TL;DR: In this article, change in any of these over time is a sign of disturbance that is important to detect and can be used to test for a temporary change, and the test statistics are based on the efficient score vector.
Journal ArticleDOI
Multivariate Kendall's tau for change-point detection in copulas
TL;DR: In this article, two multivariate extensions of Kendall's measure of association are used to detect a change in the dependence structure of a series of multivariate observations, and two estimators of the time of change are proposed and their efficiency is carefully studied.
Journal ArticleDOI
Rates of convergence for U-statistic processes and their bootstrapped versions
Edit Gombay,Lajos Horváth +1 more
TL;DR: It is shown that the bootstrap approximation for U-statistics is as good as the large sample approximations using Gaussian processes, however, the boot strap approximation is much better when the limit distributions are extreme values.
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Testing for Changes in Kendall's Tau
TL;DR: In this article, a nonparametric change-point test statistic based on Kendall's tau was proposed to detect whether the correlation between a bivariate time series and a single change point stays constant under the null hypothesis of no change by means of a new invariance principle for dependent processes.
References
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Book
Approximation Theorems of Mathematical Statistics
TL;DR: In this paper, the basic sample statistics are used for Parametric Inference, and the Asymptotic Theory in Parametric Induction (ATIP) is used to estimate the relative efficiency of given statistics.
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Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability
Lucien Le Cam,Neyman Jerzy +1 more
Book
Limit theorems in change-point analysis
Miklos Csorgo,Lajos Horváth +1 more
TL;DR: The Likelihood Approach as discussed by the authors is a nonparametric method for estimating the likelihood of a given hypothesis in a linear model with respect to a given set of observations, i.e., dependent observations.
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Inference about the change-point in a sequence of binomial variables
TL;DR: In this paper, the problem of making inference about the point in a sequence of zero-one variables at which the binomial parameter changes is discussed, and the asymptotic distribution of the maximum likelihood estimate of the change-point is derived in computable form using random walk results.
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Dependent Central Limit Theorems and Invariance Principles
TL;DR: In this article, central limit theorems for martingales and near-martingales without the existence of moments or the full Lindeberg condition were proved and extended to invariance principles with a discussion of random and nonrandom norming.