Estimating Semi-Parametric Missing Values with Iterative Imputation

Estimating Semi-Parametric Missing Values with Iterative Imputation

Shichao Zhang
Copyright: © 2010 |Pages: 10
DOI: 10.4018/jdwm.2010070101
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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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There are at least three different ways of dealing with missing data based on Little and Rubin (2002): single imputation, multiple imputation, and iterative procedure.

Single imputation strategies provide a single estimate for each missing data value. Many methods for imputing missing values are single imputation methods, such as, C4.5 algorithm, kNN method, and so on. We can partition single imputation methods into parametric methods and nonparametric ones. The parametric regression imputation methods are superior if a dataset can be adequately modeled parametrically, or if users can correctly specify the parametric forms for the dataset. Non-parametric imputation (Qin et al., 2007) offers a nice alternative if users have no idea on the actual distribution of a dataset because the method can provide superior fits by capturing structure in datasets. While much work focus on modeling data by parametric or nonparametric approaches, in Engle et al. (1986) have studied the semi-parametric model. They model the electricity demand jdwm.2010070101.m01 as the sum of a smooth function jdwm.2010070101.m02 of monthly temperature t, and a linear function of jdwm.2010070101.m03 and jdwm.2010070101.m04, as well as 11 monthly dummy variables jdwm.2010070101.m05, to build a semi-parametric model firstly. In fact, semi-parametric model is more ordinary in real application than nonparametric model or parametric model because we always contain a little but no all information on our datasets, however, there are a little literatures, such as, Nikulin (2008), focusing on this issue because of the analysis complexity, in this paper, we introduce SIIA algorithm to model the partial parametric model for filling up iteratively missing target values.

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