Bootstrapping sequential change-point tests for linear regression

Hušková, Marie; Kirch, Claudia

doi:10.1007/s00184-011-0347-7

Cited by 46 publications

(41 citation statements)

References 12 publications

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“…We adopt the approach of Chu, Stinchcombe, and White (1996) that sequentially monitors the stability of model parameters based on historical sample. For relevant literatures, see Berkes, Gombay, Horvárh, and Kokoszaka (2004), Chu et al (1996), Horváth, Hušková, Kokoszka, and Steinebach (2004), Hušková and Kirch (2012) and Na et al (2011), and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Monitoring test for stability of copula parameter in time series

Lee

2014

Journal of the Korean Statistical Society

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a b s t r a c tIn this paper, we consider a monitoring procedure to detect changes of the copula parameter of strong mixing processes. We propose two monitoring procedures based on the cumulative sums of scores evaluated at consistent copula parameter estimates and their fluctuations. We investigate the asymptotic properties of our monitoring procedures under both the null of no change in the copula parameter and its alternative. We also illustrate a simulation study and a real data analysis.

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Section: Introductionmentioning

confidence: 99%

Monitoring test for stability of copula parameter in time series

Lee

2014

Journal of the Korean Statistical Society

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“…Various bootstrap schemes for specific situations have also been suggested (see e.g. Kirch, 2008; Capizzi & Masarotto, 2009; Chatterjee & Qiu, 2009; Hušková & Kirch, 2010). Further, some non‐parametric charts which account for the estimation error in past data have been proposed, see Chakraborti & Graham (2007) and references therein.…”

Section: Introductionmentioning

confidence: 99%

Guaranteed Conditional Performance of Control Charts via Bootstrap Methods

Gandy

Kvaløy

2013

Scandinavian J Statistics

127

151

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To use control charts in practice, the in‐control state usually has to be estimated. This estimation has a detrimental effect on the performance of control charts, which is often measured by the false alarm probability or the average run length. We suggest an adjustment of the monitoring schemes to overcome these problems. It guarantees, with a certain probability, a conditional performance given the estimated in‐control state. The suggested method is based on bootstrapping the data used to estimate the in‐control state. The method applies to different types of control charts, and also works with charts based on regression models. If a non‐parametric bootstrap is used, the method is robust to model errors. We show large sample properties of the adjustment. The usefulness of our approach is demonstrated through simulation studies.

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“…A wide range of literature has been published on bootstrapping in the change point problem, e.g., [9] or [10]. We build up the bootstrap test on the resampling with replacement of row vectors trp e i,1 , .…”

Section: Bootstrap and Hypothesis Testingmentioning

confidence: 99%

Testing structural changes in panel data with small fixed panel size and bootstrap

Peštová

Pešta

2014

Metrika

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Panel data of our interest consist of a moderate or relatively large number of panels, while the panels contain a small number of observations. This paper establishes testing procedures to detect a possible common change in means of the panels. To this end, we consider a ratio type test statistic and derive its asymptotic distribution under the no change null hypothesis. Moreover, we prove the consistency of the test under the alternative. The main advantage of such an approach is that the variance of the observations neither has to be known nor estimated. On the other hand, the correlation structure is required to be calculated. To overcome this issue, a bootstrap technique is proposed in the way of a completely data driven approach without any tuning parameters. The validity of the bootstrap algorithm is shown. As a byproduct of the developed tests, we introduce a common break point estimate and prove its consistency. The results are illustrated through a simulation study. An application of the procedure to actuarial data is presented. Keywords Change point¨Panel data¨Change in mean¨Fixed panel sizeS hort panels¨Ratio type statistics¨Bootstrap Mathematics Subject Classification (2000) 62H15¨62H10¨62E206 2P05¨62F40

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Bootstrapping sequential change-point tests for linear regression

Cited by 46 publications

References 12 publications

Monitoring test for stability of copula parameter in time series

Monitoring test for stability of copula parameter in time series

Guaranteed Conditional Performance of Control Charts via Bootstrap Methods

Testing structural changes in panel data with small fixed panel size and bootstrap

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