Testing for changes using permutations of U-statistics

Horváth, Lajos; Hušková, Marie

doi:10.1016/j.jspi.2004.01.001

Cited by 24 publications

(12 citation statements)

References 8 publications

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“…In the following, we define a permuted U-statistic to approximate the null distribution of Q n (·), which is similar to that of Horváth and Hušková [21] .…”

Section: Resultsmentioning

confidence: 99%

Inference of change-point in single index models

Cao

Wang

et al. 2008

Sci. China Ser. A-Math.

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Single index models are widely used in medicine, econometrics and some other fields.In this paper, we consider the inference of a change point problem in single index models. Based on density-weighted average derivative estimation (ADE) method, we propose a statistic to test whether a change point exists or not. The null distribution of the test statistic is obtained using a permutation technique. The permuted statistic is rigorously shown to have the same distribution in the limiting sense under both null and alternative hypotheses. After the null hypothesis of no change point is rejected, an ADE-based estimate of the change point is proposed under assumption that the change point is unique. A simulation study confirms the theoretical results.

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“…In the following, we define a permuted U-statistic to approximate the null distribution of Q n (·), which is similar to that of Horváth and Hušková [21] .…”

Section: Resultsmentioning

confidence: 99%

Inference of change-point in single index models

Cao

Wang

et al. 2008

Sci. China Ser. A-Math.

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“…As far as the resampling technique is concerned, one finds approaches based on permutations of the original sequence (see e.g. Antoch and Hušková, 2001;Horváth and Hušková, 2005;Horváth and Shao, 2007) and approaches that use a weighted bootstrap based on multiplier central limit theorems (see e.g. Horváth, 1999, 2002).…”

Section: Introductionmentioning

confidence: 99%

Nonparametric tests for change-point detection à la Gombay and Horváth

Holmes

Kojadinovic

Quessy

2013

Journal of Multivariate Analysis

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International audienceThe nonparametric test for change-point detection proposed by Gombay and Horváth is revisited and extended in the broader setting of empirical process theory. The resulting testing procedure for potentially multivariate observations is based on a sequential generalization of the functional multiplier central limit theorem and on modifications of Gombay and Horváth's seminal approach that appears to improve the finite-sample behavior of the tests. A large number of candidate test statistics based on processes indexed by lower-left orthants and half-spaces are considered and their performance is studied through extensive Monte Carlo experiments involving univariate, bivariate and trivariate data sets. Finally, practical recommendations are provided and the tests are illustrated on trivariate hydrological data. © 2012 Elsevier Inc

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“…Testing (1) is a problem with a long on-going tradition, and occurs in diverse fields such as biostatistics (see Page, 1955;Worsley, 1986;Blackstone, 2004), econometrics (see Dufour, 1982;Krämer, Ploberger and Alt, 1988;Zeileis et al, 2005), mathematical statistics (see Csörgő and Horváth, 1997;Hjort and Koning, 2002;Horváth and Hušková, 2005), sequential analysis (see Lai, 2001), simulation (see Schruben, 1982), and monitoring and surveillance (see Box and Luceño, 1997;Frisen, 2003). koning@few.eur.nl The problem also occurs in statistical process control (see Stoumbos et al, 2000;Woodall, 2000).…”

Section: Introductionmentioning

confidence: 99%