Results 171 to 180 of about 204,036 (213)

Joint Imputation of General Data

Journal of Survey Statistics and Methodology, 2023
Abstract High-dimensional complex survey data of general structures (e.g., containing continuous, binary, categorical, and ordinal variables), such as the US Department of Defense’s Health-Related Behaviors Survey (HRBS), often confound procedures designed to impute any missing survey data.
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Deep Imputation of Temporal Data

2019 IEEE International Conference on Healthcare Informatics (ICHI), 2019
Predictive modeling in healthcare has shown promise in various settings, such as early diagnosis, discovery of genotypephenotype associations, and the optimization of medical resource allocations [1]. Due to their data-driven nature, the effectiveness of these studies heavily relies on the quality of the collected data.
Chao Yan 0004, Cheng Gao, Xinmeng Zhang, You Chen 0001, Bradley A. Malin   +4 more
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Missing Data Imputation Techniques

International Journal of Business Intelligence and Data Mining, 2007
Intelligent data analysis techniques are useful for better exploring real-world data sets. However, the real-world data sets always are accompanied by missing data that is one major factor affecting data quality. At the same time, good intelligent data exploration requires quality data.
Qinbao Song, Martin J. Shepperd
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Missing Data and Multiple Imputation

JAMA Pediatrics, 2013
Missing data can result in biased estimates of the association between an exposure X and an outcome Y. Even in the absence of bias, missing data can hurt precision, resulting in wider confidence intervals. Analysts should examine the missing data pattern and try to determine the causes of the missingness.
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Classification Uncertainty of Multiple Imputed Data

2015 IEEE Symposium Series on Computational Intelligence, 2015
Every classification model contains uncertainty. This uncertainty can be distributed evenly or into certain areas of feature space. In regular classification tasks, the uncertainty can be estimated from posterior probabilities. On the other hand, if the data set contains missing values, not all classifiers can be used directly.
Tuomo Alasalmi, Heli Koskimäki, Jaakko Suutala, Juha Röning   +3 more
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Multiple imputation for missing data†‡

Research in Nursing & Health, 2002
AbstractMissing data occur frequently in survey and longitudinal research. Incomplete data are problematic, particularly in the presence of substantial absent information or systematic nonresponse patterns. Listwise deletion and mean imputation are the most common techniques to reconcile missing data.
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IMPUTING MISSING DATA

Journal of the American Academy of Child & Adolescent Psychiatry, 2004
Calvin D, Croy, Douglas K, Novins
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Imputation and Missing Data

2017
The presence of missing data is a big challenge for statisticians, especially if the distribution of the missing values is not completely random. Analysis performed on datasets with missing data can lead to erroneous conclusions and significant bias in the results.
Amir Momeni, Matthew Pincus, Jenny Libien   +2 more
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Imputation of Missing Data

2011
Below is a subset of data from a smoking cessation study for smokers newly diagnosed with cancer.Patients were assessed for anxiety and depression at baseline using the Hospital Anxiety and Depression Scale (Zigmond and Snaith 1983), at least 7 days before they were hospitalized for surgery.
Yuelin Li, Jonathan Baron
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An Interactive Data Imputation System

2022
Yangyang Wu, Xiaoye Miao, Yuchen Peng, Lu Chen 0001, Yunjun Gao, Jianwei Yin   +5 more
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