Missing Data and Systematic Bias
نویسندگان
چکیده
منابع مشابه
Missing data imputation in multivariable time series data
Multivariate time series data are found in a variety of fields such as bioinformatics, biology, genetics, astronomy, geography and finance. Many time series datasets contain missing data. Multivariate time series missing data imputation is a challenging topic and needs to be carefully considered before learning or predicting time series. Frequent researches have been done on the use of diffe...
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The study analyzes the extent to which student self-reported data are biased and what variables can predict the degree of the bias. A variable that students feel more sensitive about is compared in terms of reporting bias to other less sensitive variables. The reporting bias is significant only for the sensitive variable. The study explains the reporting bias for the sensitive variable on the b...
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In the classical data envelopment analysis (DEA) models, inputs and outputs are assumed as known variables, and these models cannot deal with unknown amounts of variables directly. In recent years, there are few researches on handling missing data. This paper suggests a new interval based approach to apply missing data, which is the modified version of Kousmanen (2009) approach. First, the prop...
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OBJECTIVES To describe how systematic reviewers are reporting missing data for dichotomous outcomes, handling them in the analysis and assessing the risk of associated bias. METHODS We searched MEDLINE and the Cochrane Database of Systematic Reviews for systematic reviews of randomised trials published in 2010, and reporting a meta-analysis of a dichotomous outcome. We randomly selected 98 Co...
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ژورنال
عنوان ژورنال: American Journal of Public Health
سال: 2017
ISSN: 0090-0036,1541-0048
DOI: 10.2105/ajph.2017.303969