Results 11 to 20 of about 313,820 (280)
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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Evaluating Proteomics Imputation Methods with Improved Criteria. [PDF]
J Proteome Res, 2023 AbstractQuantitative measurements produced by tandem mass spectrometry proteomics experiments typically contain a large proportion of missing values. This missingness hinders reproducibility, reduces statistical power, and makes it difficult to compare across samples or experiments.
Harris L, Fondrie WE, Oh S, Noble WS.
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Evaluating imputation methods for single-cell RNA-seq data [PDF]
BMC Bioinformatics, 2023 Background Single-cell RNA sequencing (scRNA-seq) enables the high-throughput profiling of gene expression at the single-cell level. However, overwhelming dropouts within data may obscure meaningful biological signals.
Yi Cheng +4 more
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A model-agnostic framework for dataset-specific selection of missing value imputation methods in pain-related numerical data [PDF]
Canadian Journal of PainMissing value imputation is a routine step in biomedical data analysis, yet techniques are often not tailored to specific datasets. We propose a systematic framework for selecting imputation methods customized for the unique characteristics of cross ...
Jörn Lötsch, Alfred Ultsch
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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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Multiple imputation methods for missing multilevel ordinal outcomes. [PDF]
BMC Med Res Methodol, 2023 AbstractBackgroundMultiple imputation (MI) is an established technique for handling missing data in observational studies. Joint modelling (JM) and fully conditional specification (FCS) are commonly used methods for imputing multilevel data. However, MI methods for multilevel ordinal outcome variables have not been well studied, especially when cluster
Dong M, Mitani A.
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Imputation methods for mixed datasets in bioarchaeology. [PDF]
Archaeol Anthropol SciAbstractMissing data is a prevalent problem in bioarchaeological research and imputation could provide a promising solution. This work simulated missingness on a control dataset (481 samples × 41 variables) in order to explore imputation methods for mixed data (qualitative and quantitative data). The tested methods included Random Forest (RF), PCA/MCA,
Ryan-Despraz J, Wissler A.
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Analyzing Coarsened and Missing Data by Imputation Methods. [PDF]
Stat MedABSTRACTIn various missing data problems, values are not entirely missing, but are coarsened. For coarsened observations, instead of observing the true value, a subset of values ‐ strictly smaller than the full sample space of the variable ‐ is observed to which the true value belongs.
van der Burg LLJ +6 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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