"Score tests for heteroscedasticity in wavelet regression" by Zongwu Cai, Clifford M. Hurvich et al.
 

Score tests for heteroscedasticity in wavelet regression

Abstract

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.

Department(s)

Mathematics

Document Type

Article

DOI

https://doi.org/10.1093/biomet/85.1.229

Keywords

De-noising, Signal extraction, Thresholding

Publication Date

1-1-1998

Journal Title

Biometrika

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