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Minimax estimation of sharp change points. (English) Zbl 0929.62039
Summary: We define the sharp change point problem as an extension of earlier problems in change point analysis related to nonparametric regression. As particular cases, these include estimation of jump points in smooth curves. More generally, we give a systematic treatment of the correct rate of convergence for estimating the position of a “cusp” of an arbitrary order. We propose a test function for the local regularity of a signal that characterizes such a point as a global maximum.
In the sample implementation of our method, from observations of the signal at discrete time positions \(i/n\), \(i=1,\dots,n\), we use a wavelet transformation to approximate the position of the change point in the no-noise case. We study the noise effect, in the worst case scenario over a wide class of functions having a unique irregularity of “order \(\alpha\)” and propose a sequence of estimators which converge at the rate \(n^{1/(1+2\alpha)}\), as \(n\) tends to infinity. Finally we analyze the likelihood ratio of the problem and show that this is actually the minimax rate of convergence. Examples of thresholding empirical wavelet coefficients to estimate the position of sharp change points are also presented.

62G05 Nonparametric estimation
62G08 Nonparametric regression and quantile regression
62C20 Minimax procedures in statistical decision theory
65C60 Computational problems in statistics (MSC2010)
Full Text: DOI
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