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8 articles for “Imputation Methods”
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Research ArticleOpen accessIn the keywordsRadial Basis Function Network Dependent Exclusive Mutual Interpolation for Missing Value ImputationR. S. Somasundaram, R. NedunchezhianThe success of data mining relies on the purity of the data set. Before performing the data mining, the data has to be cleaned. An unprocessed data set may contain noisy or missing values which is a critical research issue in the pre-processing stage. Imputation methods are being used to solve the missing value problems. In this proposed work, a … …Journal of Computer Science, Vol. 9, Issue 3 · 24 Apr 2013 · 7,002 accessesView PDFCite
Review ArticleOpen accessIn the abstractBibliometric Analysis of Missing Value Imputation (MVI) Research Using Python and VOSviewer: Trends and Future DirectionsSamsul Arifin, Muhammad Faisal, Edwin Kristianto Sijabat et al.Incomplete data is a pervasive issue in data science, posing significant challenges for statistical analysis and machine learning applications. Missing Value Imputation (MVI) has thus emerged as a critical area of research aimed at mitigating data quality issues and improving decision-making accuracy. This study presents a comprehensive bibliometric analysis of MVI research using Scopus-indexed publications from 2000 to 2024. The … …Journal of Computer Science, Vol. 21, Issue 12 · 20 Jan 2026 · 1,961 accessesView PDFCite
Research ArticleOpen accessIn the abstractLow-Sampling Trajectory Reconstruction using Criteria-Based Routing over a GraphEdison Ospina, Francisco Moreno, Jaime GuzmánLocation-based services mainly provide geo-location data. However, a moving object’s detailed trajectory route is lost when there is low-sampling of these location data. Previous works have been developed in order to find the possible trajectories by using the location history logged by users. These methods can be considered as reconstruction or imputation processes. In this study, we reconstruct trajectories using … …American Journal of Applied Sciences, Vol. 13, Issue 2 · 3 Feb 2016 · 7,455 accessesView PDFCite
Research ArticleOpen accessIn the abstractGeneral Linear Models in a Missing Outcome Environment of Clinical Trials Incorporating with Splines for Time-Invariant Continuous AdjustmentMinjeong Park, Dejian Lai, Xianglin L. Du et al.Missing data is a common occurrence in longitudinal studies of health care research. Although many studies have shown the potential usefulness of current missing analyses, e.g., (1) Complete Case (CC) analysis; (2) imputation methods such as Last Observation Carried Forward (LOCF), multiple imputations, Expectation-Maximization algorithm approach; and (3) methods using all available data such as linear mixed model and generalized … …Current Research in Biostatistics, Vol. 5, Issue 1 · 18 Aug 2015 · 7,401 accessesView PDFCite
Research ArticleOpen accessIn the abstractMethods for Computing Missing Item Response in Psychometric Scale ConstructionOhidul Islam SiddiquiTherapeutic potential of a new antidepressant drug is evaluated frequently based on multi-item psychometric scales. The total score of a psychometric scale is calculated based on the responses of multiple-items, in which each item is scored on a likert scale. Missing responses in some of the items are inevitable and hence it is a problem in calculating the total score … …Current Research in Biostatistics, Vol. 5, Issue 1 · 29 May 2015 · 15,648 accessesView PDFCite
Research ArticleOpen accessIn the abstractMISSING DATA IMPUTATION USING WEIGHTED OF REGIME SWITCHING MEAN AND REGRESSIONJumlong Vongprasert, Bhusana PremanodeMissing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. The purpose of this work were first to develop the Weighted of Regime Switching Mean and Regression (WRSMRI) for missing data estimation and secondly to compare its efficiency of estimation and statistical power of a … …Journal of Mathematics and Statistics, Vol. 10, Issue 2 · 3 May 2014 · 6,704 accessesView PDFCite
Research ArticleOpen accessIn the abstractA MIXTURE DROPOUT MECHANISM IN A LONGITUDINAL STUDY WITH TWO TIME POINTS: A METHADONE STUDYZohreh Toghrayee, Parvin Jalili, Heng Chin Low, Ardavan TaghvaOne of the most important issues that confront statisticians in longitudinal studies is dropouts. A variety of reasons may lead to withdrawal from a study and produce two different missingness mechanisms, namely, missing at random and non-ignorable dropouts. Nevertheless, none of these mechanisms is tenable in most studies. In addition, it may be that not all of dropouts are nonignorable. … …Journal of Mathematics and Statistics, Vol. 10, Issue 3 · 26 Apr 2014 · 6,724 accessesView PDFCite
Research ArticleOpen accessIn the abstractAN EFFECTIVE TECHNIQUE OF MULTIPLE IMPUTATION IN NONPARAMETRIC QUANTILE REGRESSIONYanan Hu, Qianqian Zhu, Maozai TianIn this study, we consider the nonparametric quantile regression model with the covariates Missing at Random (MAR). Multiple imputation is becoming an increasingly popular approach for analyzing missing data, which combined with quantile regression is not well-developed. We propose an effective and accurate two-stage multiple imputation method for the model based on the quantile regression, which consists of initial imputation … …Journal of Mathematics and Statistics, Vol. 10, Issue 1 · 4 Jan 2014 · 6,813 accessesView PDFCite