Results 281 to 290 of about 287,343 (334)

Missing Data Imputation

2012
Missing data in clinical research data is often a real problem. As an example, a 35 patient data file of 3 variables consists of 3 × 35 = 105 values if the data are complete. With only 5 values missing (1 value missing per patient) 5 patients will not have complete data, and are rather useless for the analysis.
Ton J. Cleophas, Aeilko H. Zwinderman
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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 Shepperd
openaire   +1 more source

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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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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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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Missing phenotype data imputation in pedigree data analysis

Genetic Epidemiology, 2007
AbstractMapping complex traits or phenotypes with small genetic effects, whose phenotypes may be modulated by temporal trends in families are challenging. Detailed and accurate data must be available on families, whether or not the data were collected over time.
Brooke L, Fridley, Mariza, de Andrade
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Imputation of missing healthcare data

2017 20th International Conference of Computer and Information Technology (ICCIT), 2017
In the field of data mining, missing values has always been a crucial factor. Incorrect imputation of missing values could lead to inaccurate research as well as wrong predictions. Developing a generalized imputation strategy that can be used across a variety of dataset is very hard.
Mohaimanul Hoque Chowdhury, Muhammad Kamrul Islam, Shahidul Islam Khan   +2 more
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[Imputation of missing data].

Nederlands tijdschrift voor geneeskunde, 2013
In medical research missing data are sometimes inevitable. Different missingness mechanisms can be distinguished: (a) missing completely at random; (b) missing by design; (c) missing at random, and (d) missing not at random. If participants with missing data are excluded from statistical analyses, this can lead to biased study results and loss of ...
Ralph C A, Rippe, Martin, den Heijer, Saskia, le Cessie   +2 more
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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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