Results 21 to 30 of about 1,718,863 (277)
Lines missing every random point [PDF]
Computability, 2014 Abstract We prove that there is, in every direction in Euclidean space, a line that misses every computably random point. We also prove that there exist, in every direction in Euclidean space, arbitrarily long line segments missing every double exponential time random point.
Jack H. Lutz, Neil Lutz
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Regularized Mislevy-Wu Model for Handling Nonignorable Missing Item Responses
Information, 2023 Missing item responses are frequently found in educational large-scale assessment studies. In this article, the Mislevy-Wu item response model is applied for handling nonignorable missing item responses.
Alexander Robitzsch
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Journal of the College of Community Physicians, 2018 Using correct methods for prevention, analysis and treatment of missing data is essential in preserving the validity of scientific research. In spite of this, issues related to missing data and non-response bias are found to be inadequately discussed in ...
P. K. B. Mahesh +4 more
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Diagnosing and Handling Common Violations of Missing at Random [PDF]
Psychometrika, 2023 Ignorable likelihood (IL) approaches are often used to handle missing data when estimating a multivariate model, such as a structural equation model. In this case, the likelihood is based on all available data, and no model is specified for the missing data mechanism.
Feng Ji +2 more
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BMC Medical Research Methodology, 2017 Background Within epidemiological and clinical research, missing data are a common issue and often over looked in publications. When the issue of missing observations is addressed it is usually assumed that the missing data are ‘missing at random’ (MAR).
M. Smuk, J. R. Carpenter, T. P. Morris
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Impute the missing data using retrieved dropouts
BMC Medical Research Methodology, 2022 Background In the past few decades various methods have been proposed to handle missing data of clinical studies, so as to assess the robustness of primary results.
Shuai Wang, Haoyan Hu
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Regression-Based Approach to Test Missing Data Mechanisms
Data, 2022 Missing data occur in almost all surveys; in order to handle them correctly it is essential to know their type. Missing data are generally divided into three types (or generating mechanisms): missing completely at random, missing at random, and missing ...
Serguei Rouzinov, André Berchtold
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Comprehensive analysis of missing data imputation in clinical time-series: challenges, risks, and practical solutions [PDF]
PeerJ Computer ScienceMissing data in clinical time series is pervasive and decision-critical, arising from irregular sampling, workflow-driven measurement policies, sensor failures, and intervention-dependent monitoring.
Aasim Ayaz Wani, Fatima Abeer
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Missing data and multiple imputation in clinical epidemiological research [PDF]
, 2017 Missing data are ubiquitous in clinical epidemiological research. Individuals with missing data may differ from those with no missing data in terms of the outcome of interest and prognosis in general. Missing data are often categorized into the following
Cronin-Fenton, D +6 more
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BMC Medical Research Methodology, 2019 Background The benefit of a given treatment can be evaluated via a randomized clinical trial design. However, protocol deviations may severely compromise treatment effect since such deviations often lead to missing values.
Abdul-Karim Iddrisu, Freedom Gumedze
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