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A comparison of imputation methods for categorical data
Informatics in Medicine Unlocked, 2023 Objectives: Missing data is commonplace in clinical databases, which are being increasingly used for research. Without giving any regard to missing data, results from analysis may become biased and unrepresentative.
Shaheen MZ. Memon +2 more
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Deep Learning Methods for Omics Data Imputation
Biology, 2023 One common problem in omics data analysis is missing values, which can arise due to various reasons, such as poor tissue quality and insufficient sample volumes.
Lei Huang +6 more
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A Benchmark for Data Imputation Methods [PDF]
Frontiers in Big Data, 2021 With the increasing importance and complexity of data pipelines, data quality became one of the key challenges in modern software applications. The importance of data quality has been recognized beyond the field of data engineering and database ...
Sebastian Jäger +2 more
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Outcome-sensitive multiple imputation: a simulation study [PDF]
BMC Medical Research Methodology, 2017 Background Multiple imputation is frequently used to deal with missing data in healthcare research. Although it is known that the outcome should be included in the imputation model when imputing missing covariate values, it is not known whether it should
Evangelos Kontopantelis +3 more
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yaImpute: An R Package for kNN Imputation [PDF]
Journal of Statistical Software, 2007 This article introduces yaImpute, an R package for nearest neighbor search and imputation. Although nearest neighbor imputation is used in a host of disciplines, the methods implemented in the yaImpute package are tailored to imputation-based forest ...
Andrew O. Finley, Nicholas L. Crookston
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Efficient Difference and Ratio-Type Imputation Methods under Ranked Set Sampling
Axioms, 2023 It is well known that ranked set sampling (RSS) is more efficient than simple random sampling (SRS). Furthermore, the presence of missing data vitiates the conventional results.
Shashi Bhushan +3 more
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Mathematics, 2023 The Industrial Internet of Things (IIoT), which integrates sensors into the manufacturing system, provides new paradigms and technologies to industry.
Minh Hung Ho +7 more
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A Bayesian Approach for Imputation of Censored Survival Data
Stats, 2022 A common feature of much survival data is censoring due to incompletely observed lifetimes. Survival analysis methods and models have been designed to take account of this and provide appropriate relevant summaries, such as the Kaplan–Meier plot and the ...
Shirin Moghaddam +2 more
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A Systematic Literature Review On Missing Values: Research Trends, Datasets, Methods and Frameworks [PDF]
E3S Web of Conferences, 2023 Handling of missing values in data analysis is the focus of attention in various research fields. Imputation is one method that is commonly used to overcome this problem of missing data.
Setiawan Ismail +2 more
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A Pragmatic Ensemble Strategy for Missing Values Imputation in Health Records
Entropy, 2022 Pristine and trustworthy data are required for efficient computer modelling for medical decision-making, yet data in medical care is frequently missing.
Shivani Batra +5 more
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