Publication Type:

Journal Article

Source:

Communications in Statistics-Simulation and Computation, Taylor & Francis, Volume 44, Number 6, p.1532–1544 (2015)

Keywords:

Analysis-prior, Heteroscedasticity, Piecewise constant functions, Primary 62G08, Secondary 65T60, Undecimated Haar Transform

Abstract:

We propose a method in order to maximize the accuracy in the estimation of piecewise constant and piecewise smooth variance functions in a nonparametric heteroscedastic fixed design regression model. The difference-based initial estimates are obtained from the given observations. Then an estimator is constructed by using iterative regularization method with the analysis-prior undecimated three-level Haar transform as regularizer term. We notice that this method shows better results in the mean square sense over an existing adaptive estimation procedure considering all the standard test functions used in addition to the functions that we target. Some simulations and comparisons with other methods are conducted to assess the performance of the proposed method.

Cite this Research Publication

T. Palanisamy and Ravichandran, J., “Variance Estimation in Heteroscedastic Models by Undecimated Haar Transform”, Communications in Statistics-Simulation and Computation, vol. 44, pp. 1532–1544, 2015.