Results 21 to 30 of about 184,759 (266)
Modern multiple imputation with functional data [PDF]
Stat, 2021 This work considers the problem of fitting functional models with sparsely and irregularly sampled functional data. It overcomes the limitations of the state‐of‐the‐art methods, which face major challenges in the fitting of more complex non‐linear models.
Aniruddha Rajendra Rao, Matthew Reimherr
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Nonparametric Imputation by Data Depth [PDF]
Journal of the American Statistical Association, 2019 We present single imputation method for missing values which borrows the idea of data depth---a measure of centrality defined for an arbitrary point of a space with respect to a probability distribution or data cloud. This consists in iterative maximization of the depth of each observation with missing values, and can be employed with any properly ...
Mozharovskyi, Pavlo +2 more
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BMC Medical Research Methodology, 2020 Background Missing data are common in statistical analyses, and imputation methods based on random forests (RF) are becoming popular for handling missing data especially in biomedical research.
Shangzhi Hong, Henry S. Lynn
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Fairness in Missing Data Imputation
CoRR, 2021 Missing data are ubiquitous in the era of big data and, if inadequately handled, are known to lead to biased findings and have deleterious impact on data-driven decision makings. To mitigate its impact, many missing value imputation methods have been developed.
Yiliang Zhang, Qi Long
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Comparison of Performance of Data Imputation Methods for Numeric Dataset
Applied Artificial Intelligence, 2019 Missing data is common problem faced by researchers and data scientists. Therefore, it is required to handle them appropriately in order to get better and accurate results of data analysis.
Anil Jadhav +2 more
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Multiple imputation of maritime search and rescue data at multiple missing patterns.
PLoS ONE, 2021 Based on the missing situation and actual needs of maritime search and rescue data, multiple imputation methods were used to construct complete data sets under different missing patterns.
Guobo Wang +4 more
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JMIR mHealth and uHealth, 2020 BackgroundBody weight variability (BWV) is common in the general population and may act as a risk factor for obesity or diseases. The correct identification of these patterns may have prognostic or predictive value in clinical and research settings. With
Turicchi, Jake +7 more
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Impact of Missing Data on Data Quality in Social Research
Соціологічні студіїMissing data is a common issue in quantitative social research that negatively affects the data quality. This article explores the consequences of missing data, outlining the potential issues it may pose and emphasizing the importance of properly ...
Yaroslav Kostenko
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Multiply-Imputed Synthetic Data: Advice to the Imputer [PDF]
Journal of Official Statistics, 2017 Abstract Several statistical agencies have started to use multiply-imputed synthetic microdata to create public-use data in major surveys. The purpose of doing this is to protect the confidentiality of respondents’ identities and sensitive attributes, while allowing standard complete-data analyses of microdata.
Loong, Bronwyn, Rubin, Donald B
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Context-Aware Data Imputation: Application of Domain-Agnostic Deep Imputation Network
IEEE AccessData imputation (DI) is a crucial task to manage missing data across different domains, such as healthcare and finance. Traditional imputation methods often fail to account for contextual nuances within specific domains due to the heterogeneity of data ...
Mohammed Gh. Al Zamil +1 more
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