Publication Type:

Journal Article


Communications in Statistics: Simulation and Computation, Taylor and Francis Inc., Volume 43, Number 10, p.2213-2224 (2014)



Generalized coiflets, Heteroscedasticity, Mathematical models, Nondyadic points, Regression analysis, Sampling, Vanishing moment, Variance estimate


A wavelet approach is presented to estimate the variance function in heteroscedastic nonparametric regression model. The initial variance estimates are obtained as squared weighted sums of neighboring observations. The initial estimator of a smooth variance function is improved by means of wavelet smoothers under the situation that the samples at the dyadic points are not available. Since the traditional wavelet system for the variance function estimation is not appropriate in this situation, we demonstrate that the choice of the wavelet system is significant to have better performance. This is accomplished by choosing a suitable wavelet system known as the generalized coiflets. We conduct extensive simulations to evaluate finite sample performance of our method. We also illustrate our method using a real dataset. Copyright © 2014 Taylor & Francis Group, LLC.


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Cite this Research Publication

T. Palanisamy and Ravichandran, J., “Estimation of variance function in heteroscedastic regression models by generalized coiflets”, Communications in Statistics: Simulation and Computation, vol. 43, pp. 2213-2224, 2014.