Results 11 to 20 of about 318,466 (281)
Imputation methods for mixed datasets in bioarchaeology [PDF]
Archaeological and Anthropological SciencesAbstractMissing 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,
Jessica Ryan-Despraz, Amanda Wissler
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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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The impact of misclassifications and outliers on imputation methods [PDF]
Journal of Applied StatisticsMany imputation methods have been developed over the years and tested mostly under ideal settings. Surprisingly, there is no detailed research on how imputation methods perform when the idealized assumptions about the distribution of data and/or model assumptions are partly not fulfilled.
M. Templ, Markus Ulmer
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Analyzing Coarsened and Missing Data by Imputation Methods [PDF]
Statistics in MedicineABSTRACTIn 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.
Burg, L.L.J. van der +6 more
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Multi-metric comparison of machine learning imputation methods with application to breast cancer survival [PDF]
BMC Medical Research MethodologyHandling missing data in clinical prognostic studies is an essential yet challenging task. This study aimed to provide a comprehensive assessment of the effectiveness and reliability of different machine learning (ML) imputation methods across various ...
Imad El Badisy +3 more
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Missing Data and Imputation Methods [PDF]
Anesthesia & Analgesia, 2020 Schober, Patrick, Vetter, Thomas R.
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Methods to Handle Incomplete Data
MAMC Journal of Medical Sciences, 2020 Context: The major question for data analysis is determining the appropriate analytic approach in the presence of incomplete observations. The most common solution to handle missing data in a data set is imputation, where missing values are estimated and
Vinny Johny +2 more
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TIME SERIES IMPUTATION USING VAR-IM (CASE STUDY: WEATHER DATA IN METEOROLOGICAL STATION OF CITEKO)
Barekeng, 2022 Univariate imputation methods are defined as imputation methods that only use the information of the target variable to estimate missing values.
Muhammad Edy Rizal +2 more
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Assessment of genotype imputation methods [PDF]
BMC Proceedings, 2009 Abstract Several methods have been proposed to impute genotypes at untyped markers using observed genotypes and genetic data from a reference panel. We used the Genetic Analysis Workshop 16 rheumatoid arthritis case-control dataset to compare the performance of four of these imputation methods: IMPUTE, MACH, PLINK, and fastPHASE.
Biernacka, Joanna M +9 more
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