Inference for post-change parameters after sequential CUSUM test under AR(1) model

Wu, Yanhong

doi:10.1016/j.jspi.2015.06.006

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Cited by 2 publications

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References 43 publications

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“…5, No. 5;2016 Finally, we simulate the biases for the change-point and post-change mean estimators. For the same designs given in Table 2, the simulation is replicated 5000 times and and only those stopping times with N > ν are counted to calculate the conditional expectations.…”

Section: Normal Mean Shiftmentioning

confidence: 99%

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Estimation of Change-point and Post-change Parameters after Adaptive Sequential CUSUM Test in an Exponential Family

Wu¹

2016

IJSP

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In this paper, we consider an adaptive sequential CUSUM procedure in an exponential family where the change-point and post-change parameters are estimated adaptively. It is shown that the adaptive CUSUM procedure is efficient at the first order. The conditional biases of the estimation for the change-point and post-change parameter are studied. Comparison with the classical CUSUM procedure in the normal case is made. Nile river flow and average global temperature data sets are used for demonstration.

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Section: Normal Mean Shiftmentioning

confidence: 99%

“…5, No. 5;2016 approximation. For observations X k+1 , ..., X n , define recursively the mean estimator…”

Section: Introductionmentioning

confidence: 99%

Estimation of Change-point and Post-change Parameters after Adaptive Sequential CUSUM Test in an Exponential Family

Wu¹

2016

IJSP

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Exploring the longevity risk using statistical tools derived from the Shiryaev–Roberts procedure

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Habart

Rainer³

et al. 2018

Eur. Actuar. J.

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Inference for post-change parameters after sequential CUSUM test under AR(1) model

Cited by 2 publications

References 43 publications

Estimation of Change-point and Post-change Parameters after Adaptive Sequential CUSUM Test in an Exponential Family

Estimation of Change-point and Post-change Parameters after Adaptive Sequential CUSUM Test in an Exponential Family

Exploring the longevity risk using statistical tools derived from the Shiryaev–Roberts procedure

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