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Generating Synthetic Missing Data: A Review by Missing Mechanism
IEEE Access, 2019 The performance evaluation of imputation algorithms often involves the generation of missing values. Missing values can be inserted in only one feature (univariate configuration) or in several features (multivariate configuration) at different ...
Miriam Seoane Santos +2 more
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Handling missing data: AI approach for survival prediction in lung cancer despite missing data [PDF]
npj Digital MedicineOptimal cancer prognostication combines multimodal data including biopsy results, CT scan, patient characteristics, and clinical trajectory thus far. However, many patients do not have all modalities of data available, so requiring physicians to have all
Margaret Y. Sui +3 more
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How to Generate Missing Data For Simulation Studies [PDF]
Tutorials in Quantitative Methods for Psychology, 2023 Missing data are common in psychological and educational research. With the improvement in computing technology in recent decades, more researchers have begun developing missing data techniques.
Zhang, Xijuan
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Advanced methods for missing values imputation based on similarity learning [PDF]
PeerJ Computer Science, 2021 The real-world data analysis and processing using data mining techniques often are facing observations that contain missing values. The main challenge of mining datasets is the existence of missing values.
Khaled M. Fouad +3 more
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Journal of King Saud University: Computer and Information Sciences, 2023 Clinical data are essential in the medical domain. However, their heterogeneous nature leads to many data quality problems, notably missing values, which undermine the performance of Machine Learning-based clinical systems.
Isabel Curioso +6 more
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Model Selection with Missing Data Embedded in Missing-at-Random Data
Stats, 2023 When models are built with missing data, an information criterion is needed to select the best model among the various candidates. Using a conventional information criterion for missing data may lead to the selection of the wrong model when data are not ...
Keiji Takai, Kenichi Hayashi
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Dealing with missing data in covariates: The missing indicator method [PDF]
Tutorials in Quantitative Methods for Psychology, 2022 This vignette presents the missing indicator method for handling missing data in covariates. The method unfolds through two activities, guiding students in the practical implementation of the method and comparable alternatives using statistical software.
Jolani, Shahab, Weinstein, Pawel
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Performance of Missing Data Approaches Under Nonignorable Missing Data Conditions
Methodology, 2020 Approaches for dealing with item omission include incorrect scoring, ignoring missing values, and approaches for nonignorable missing values and have only been evaluated for certain forms of nonignorability.
Steffi Pohl, Benjamin Becker
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A Comparison of Different Methods for Rainfall Imputation: A Galician Case Study
Applied Sciences, 2023 With the ultimate goal of developing models that involve the use of environmental variables, a GIS-based application is being developed that is circumscribed to the region of Galicia (Spain). Ten-minute data of six meteorological variables were collected
José Vidal-Paz +2 more
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Improving accuracy of missing data imputation in data mining
Kurdistan Journal of Applied Research, 2017 In fact, raw data in the real world is dirty. Each large data repository contains various types of anomalous values that influence the result of the analysis, since in data mining, good models usually need good data, databases in the world are not always
Nzar A. Ali, Zhyan M. Omer
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