Discrete and Continuous Missing Values by Using Mixture-Kernel-Based Iteratives

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Provided by: International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE)
Format: PDF
Proper handling of missing values is important in all analyses and is critical in some, such as time series analysis. In this paper, a new setting of missing data imputation, i.e., imputing missing data in data sets with heterogeneous attributes referred to as imputing mixture-kernel-based iterative data sets. This concept first proposes two consistent estimators for the methods in terms of classification accuracy and root square error at the different ratios by using the non-parametric algorithms.
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