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153  Articles
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Cardiovascular disease (CVD) is the leading cause of death worldwide. Primary prevention is by early prediction of the disease onset. Using laboratory data from the National Health and Nutrition Examination Survey (NHANES) in 2017-2020 timeframe (N= 7.974... see more

A missing value is a common problem of most data processing in scientific research, which results in a lack of accuracy of research results. Several methods have been applied as a missing value solution, such as deleting all data that have a missing value... see more

K-nearest neighbors (KNN) has been extensively used as imputation algorithm to substitute missing data with plausible values. One of the successes of KNN imputation is the ability to measure the missing data simulated from its nearest neighbors robustly. ... see more

K-nearest neighbors (KNN) has been extensively used as imputation algorithm to substitute missing data with plausible values. One of the successes of KNN imputation is the ability to measure the missing data simulated from its nearest neighbors robustly. ... see more

Multiple imputation (MI) is a powerful statistical method for handling missing data. Standard implementations of MI are valid under the unverifiable assumption of missing at random (MAR), which is often implausible in practice. The delta-adjustment method... see more

The purpose of this study was to evaluate the performance of multiple imputation method in case that missing observation structure is at random and completely at random from the approach of general linear mixed model. The application data of study was con... see more

The existence of missing values will really inhibit process of clustering. To overcome it, some of scientists have found several solutions. Both of them are imputation and special clustering algorithms. This paper compared the results of clustering by usi... see more

An imputation design is presented to combine classification and imputation in order to improve the quality of imputed datum. Imputation is done with completely randomized missing quantitative data and using regression trees. Media imputation techniques is... see more

Introduction: The analysis of designs of experiments with bivariate answers can be a challenge for the researcher, especially when some data of the answers are censored. Chowdhury and Aggarwala (2007) presented a set of techniques to impute values to the ... see more

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