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Robust Wavelet Regression with Randomly Missing Data

Alsaidi M. Altaher, Mohd Tahir Ismail

Abstract


This paper considers the problem of randomly missing data for robust wavelet estimation methods proposed by Altaher and Ismail (2011). The presence of such missing may pose serious problems, affect the bias and finally misleading conclusions might be drawn. In this paper we investigate the use of two imputation methods; based on a combination of bootstrap with predicted regression models. These methods use initial-bootstrap estimates iterated
through either polynomial or local polynomial regression model till convergence. Practical implementations show that these methods are easily implemented and achieve high accuracy even for large percentage of randomly missing values. The numerical achievement has been investigated through real data example with comparison to other existing
imputation methods.

Keywords


polynomial, local polynomial,bootstrap, wavelet, missing data

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