THE WAVELET DETECTION OF THE JUMP AND CUSP POINTS OF A REGRESSION FUNCTION  

作　　者：李元 谢衷洁 

出　　处：《Acta Mathematicae Applicatae Sinica》2000年第3期283-291,共9页应用数学学报（英文版）

摘　　要：Wavelets are applied to a regression model with an additive stationary noise. By checking the empirical wavelet coefficients with significantly large absolute values across fine scale levels, the jump points are detected first. Then the cusp points are identified by checking the wavelet coefficients with significantly large absolute values which are secondly large only to the previous wavelet coefficient across fine scale levels. All estimators are shown to be consistent.Wavelets are applied to a regression model with an additive stationary noise. By checking the empirical wavelet coefficients with significantly large absolute values across fine scale levels, the jump points are detected first. Then the cusp points are identified by checking the wavelet coefficients with significantly large absolute values which are secondly large only to the previous wavelet coefficient across fine scale levels. All estimators are shown to be consistent.

关 键 词：Jump and cusp points regression function stationary noises WAVELETS 

分 类 号：O29[理学—应用数学]