Score tests for heteroscedasticity in wavelet regression
We consider two score tests for heteroscedasticity in the errors of a signal-plus-noise model, where the signal is estimated by wavelet thresholding methods. The error variances are assumed to depend on observed covariates, through a parametric relationship of known form. The tests are based on the approaches of Breusch & Pagan (1979) and Koenker (1981). We establish the asymptotic validity of the tests and examine their performance in a simulation study. The Koenker test is found to perform well, in terms of both size and power.
De-noising, Signal extraction, Thresholding
Cai, Zongw, Clifford M. Hurvich, and Chih-Ling Tsai. "Score tests for heteroscedasticity in wavelet regression." Biometrika 85, no. 1 (1998): 229-233.