 Estimation of a regression function with a sharp change point using boundary waveletsCheol-Woo Park and Woo-Chul Kim Statistics & Probability Letters, 2004, vol. 66, issue 4, 435-448 Abstract: We propose a sharp change point estimator based on the differences between right and left boundary wavelet smoothers. It is constructed by applying a two-step procedure to the observed data and has the minimax convergence rate. Next, we estimate the regression function with boundary wavelets in the left and right regions of the estimated jump point separately. This method helps us to capture the feature of a discontinuity in practice. Both mean integrated squared error and mean squared error of the estimated function are derived and we then show that these rates of convergence are the same as the case in which a jump point does not exist. Simulated examples demonstrate the improved performance of the proposed methods. Keywords: Block; thresholding; Boundary; wavelets; Rate; of; convergence; Sharp; change; point; problem; Wavelet; function; estimation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:66:y:2004:i:4:p:435-448 Ordering information: This journal article can be ordered from Access Statistics for this article Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul More articles in Statistics & Probability Letters from Elsevier |
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