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Estimating Semi-Parametric Missing Values with Iterative Imputation

Estimating Semi-Parametric Missing Values with Iterative Imputation

Shichao Zhang
Copyright: © 2010 |Volume: 6 |Issue: 3 |Pages: 10
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781609609139|DOI: 10.4018/jdwm.2010070101
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MLA

Zhang, Shichao. "Estimating Semi-Parametric Missing Values with Iterative Imputation." IJDWM vol.6, no.3 2010: pp.1-10. http://doi.org/10.4018/jdwm.2010070101

APA

Zhang, S. (2010). Estimating Semi-Parametric Missing Values with Iterative Imputation. International Journal of Data Warehousing and Mining (IJDWM), 6(3), 1-10. http://doi.org/10.4018/jdwm.2010070101

Chicago

Zhang, Shichao. "Estimating Semi-Parametric Missing Values with Iterative Imputation," International Journal of Data Warehousing and Mining (IJDWM) 6, no.3: 1-10. http://doi.org/10.4018/jdwm.2010070101

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Abstract

In this paper, the author designs an efficient method for imputing iteratively missing target values with semi-parametric kernel regression imputation, known as the semi-parametric iterative imputation algorithm (SIIA). While there is little prior knowledge on the datasets, the proposed iterative imputation method, which impute each missing value several times until the algorithms converges in each model, utilize a substantially useful amount of information. Additionally, this information includes occurrences involving missing values as well as capturing the real dataset distribution easier than the parametric or nonparametric imputation techniques. Experimental results show that the author’s imputation methods outperform the existing methods in terms of imputation accuracy, in particular in the situation with high missing ratio.

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