Results 31 to 40 of about 1,318,109 (301)
Methodology, 2020 In earlier literature, multiple imputation was proposed to create balance in unbalanced designs, as an alternative to Type III sum of squares in two-way ANOVA.
Joost R. van Ginkel +1 more
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Environment International, 2022 Urinary biomarkers are commonly used in epidemiological studies as surrogates or indicators of exposure to chemical substances. Evaluating the reliability of a biomarker is highly important because use of an unreliable marker may lead to ...
Yukiko Nishihama +3 more
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Nonparametric statistical inference and imputation for incomplete categorical data [PDF]
Statistics and Its Interface, 2020 9 pages, 2 ...
Wang, Chaojie +3 more
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When Does Choice of Accuracy Measure Alter Imputation Accuracy Assessments? [PDF]
PLoS ONE, 2015 Imputation, the process of inferring genotypes for untyped variants, is used to identify and refine genetic association findings. Inaccuracies in imputed data can distort the observed association between variants and a disease.
Shelina Ramnarine +10 more
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PLoS Computational Biology, 2022 Imputing missing values is common practice in label-free quantitative proteomics. Imputation aims at replacing a missing value with a user-defined one.
Marie Chion +2 more
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PLoS Computational Biology, 2020 Genotype imputation estimates the genotypes of unobserved variants using the genotype data of other observed variants based on a collection of haplotypes for thousands of individuals, which is known as a haplotype reference panel.
Kaname Kojima +5 more
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Multiple Imputation of Missing Data in Educational Production Functions
Computation, 2022 Educational production functions rely mostly on longitudinal data that almost always exhibit missing data. This paper contributes to a number of avenues in the literature on the economics of education and applied statistics by reviewing the theoretical ...
Amira Elasra
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The Indian Journal of Agricultural Sciences, 2023 Occurrence of missing observations in mixture of qualitative and quantitative trait data is a common feature in breeding experiments. However, it becomes difficult to cluster the germplasms in presence of missing data.
RUPAM KUMAR SARKAR +3 more
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The Optimal Machine Learning-Based Missing Data Imputation for the Cox Proportional Hazard Model
Frontiers in Public Health, 2021 An adequate imputation of missing data would significantly preserve the statistical power and avoid erroneous conclusions. In the era of big data, machine learning is a great tool to infer the missing values.
Chao-Yu Guo +4 more
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ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-Temporal Graph Attention and Bidirectional Recurrent United Neural Networks [PDF]
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), 2023 Traffic data serves as a fundamental component in both research and applications within intelligent transportation systems. However, real-world transportation data, collected from loop detectors or similar sources, often contains missing values (MVs ...
Zepu Wang +4 more
semanticscholar +1 more source