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Change-point estimation under adaptive sampling. (English) Zbl 1168.62018

Summary: We consider the problem of locating a jump discontinuity (change-point) in a smooth parametric regression model with a bounded covariate. It is assumed that one can sample the covariate at different values and measure the corresponding responses. Budget constraints dictate that a total of \(n\) such measurements can be obtained. A multistage adaptive procedure is proposed, where at each stage an estimate of the change point is obtained and new points are sampled from its appropriately chosen neighborhood.
It is shown that such procedures accelerate the rate of convergence of the least squares estimate of the change-point. Further, the asymptotic distribution of the estimate is derived using empirical process techniques. The latter result provides guidelines on how to choose the tuning parameters of the multistage procedure in practice. The improved efficiency of the procedure is demonstrated using real and synthetic data. This problem is primarily motivated by applications in engineering systems.

MSC:

62F12 Asymptotic properties of parametric estimators
62J02 General nonlinear regression
62M09 Non-Markovian processes: estimation
62E20 Asymptotic distribution theory in statistics
62L12 Sequential estimation
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
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References:

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