The wavelet identification for jump points of derivative in regression model
AbstractA method is proposed to detect the number, locations and heights of jump points of the derivative in the regressive model [eta]i=f([xi]i)+[var epsilon]i, by checking if the empirical indirect wavelet coefficients of data have significantly large absolute values across fine scale levels. The consistency of the estimators is established and practical implementation is discussed. Some simulation examples are given to test our method.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 53 (2001)
Issue (Month): 2 (June)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Ogden, Todd & Parzen, Emanuel, 1996. "Data dependent wavelet thresholding in nonparametric regression with change-point applications," Computational Statistics & Data Analysis, Elsevier, vol. 22(1), pages 53-70, June.
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