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11 articles for “Data Imputation”
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Research ArticleOpen accessPhrase in the titleMISSING 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 accessEvery word in the titleApplication of Multiple Imputations to Freight Transportation Survey Data: A Case Study of Commodity Flow SurveyTerdsak Rongviriyapanich, Akachut SuppiyatrakulProblem statement: Freight transportation data was indispensable input to transportation planning. In Thailand, efforts had been put to collect freight movement data by conducting road side survey and commodity flow survey. The results of these surveys did not produce consistent volume of shipment due to limited sampling coverage and non-response. Nevertheless, freight distribution patterns, which were derived from these surveys, … …American Journal of Engineering and Applied Sciences, Vol. 4, Issue 3 · 18 Aug 2011 · 7,477 accessesView PDFCite
Review ArticleOpen accessIn the keywordsBibliometric 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 keywordsPrediction of Physicochemical Parameters in a Rainwater Harvesting and Treatment System Modeled Using the Yahtun Algorithm in Native Communities of Northern PeruEli Morales-Rojas, Edwin Adolfo Díaz Ortiz, José Emmanuel Cruz de La Cruz et al.The inhabitants of the native communities of northern Peru do not have drinking water systems, generating serious health impacts. In this sense, rainwater harvesting systems have become an alternative for these areas. Therefore, the objective of this study was to predict the physicochemical parameters in a rainwater collection and treatment system, modeled by means of a novel “Yahtun” algorithm. The … …OnLine Journal of Biological Sciences, Vol. 25, Issue 1 · 13 Feb 2025 · 5,149 accessesView PDFCite
Research ArticleOpen accessIn the keywordsMethods 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 keywordsA 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 abstractData Preparation in Machine Learning for Condition-based MaintenanceOns Masmoudi, Mehdi Jaoua, Amel Jaoua, Soumaya YacoutUsing Machine Learning (ML) prediction to achieve a successful, cost-effective, Condition-Based Maintenance (CBM) strategy has become very attractive in the context of Industry 4.0. In other fields, it is well known that in order to benefit from the prediction capability of ML algorithms, the data preparation phase must be well conducted. Thus, the objective of this paper is to investigate … …Journal of Computer Science, Vol. 17, Issue 6 · 4 Jun 2021 · 8,412 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 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