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Subtypes of the missing not at random missing data mechanism.
Psychological Methods, 2021issing values that are missing not at random (MNAR) can result from a variety of missingness processes. However, two fundamental subtypes of MNAR values can be obtained from the definition of the MNAR mechanism itself. The distinction between them deserves consideration because they have characteristic differences in how they distort relationships in ...
Brenna Gomer, Ke-Hai Yuan
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Distinguishing “Missing at Random” and “Missing Completely at Random”
The American Statistician, 1996Abstract Missing at random (MAR) and missing completely at random (MCAR) are ignorability conditions—when they hold, they guarantee that certain kinds of inferences may be made without recourse to complicated missing-data modeling. In this article we review the definitions of MAR, MCAR, and their recent generalizations.
Daniel F. Heitjan, Srabashi Basu
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A ‘missing not at random’ (MNAR) and ‘missing at random’ (MAR) growth model comparison with a buprenorphine/naloxone clinical trial [PDF]
Addiction, 2015 Sterling Mcpherson +2 more
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Tests If Dropouts Are Missed at Random
Biometrical Journal, 1998Summary: Dropouts are a common problem in longitudinal investigations where individuals are measured repeatedly over time. This holds also in a study on rheumatoid arthritis where an inception cohort was followed up over three years. The question arose whether or not these individuals caused a selection bias.
Listing, Joachim, Schlittgen, Rainer
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On missing random effects in machine learning
Communications in Statistics - Simulation and Computation, 2020The large availability of undesigned data, a by-product of chemical industrial research and manufacturing, makes it attractive the venturesome use of machine learning for its plug-and-play appeal i...
Fabio D'Ottaviano, Wenzhao Yang
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Entropy-Randomized Method for the Reconstruction of Missing Data
Automation and Remote Control, 2021zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yu. A. Dubnov +5 more
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A test of missing completely at random for longitudinal data with missing observations
Statistics in Medicine, 1997Liang and Zeger proposed a generalized estimating equations approach to the analysis of longitudinal data. Their models assume that missing observations are missing completely at random in the sense of Rubin. However, when this assumption does not hold, their analysis may yield biased results.
T, Park, S Y, Lee
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Social Recommendation with Missing Not at Random Data
2018 IEEE International Conference on Data Mining (ICDM), 2018With the explosive growth of online social networks, many social recommendation methods have been proposed and demonstrated that social information has potential to improve the recommendation performance. However, existing social recommendation methods always assume that the data is missing at random (MAR) but this is rarely the case.
Jiawei Chen 0007 +5 more
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Dimension reduction with missing response at random
Computational Statistics & Data Analysis, 2014zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xu Guo +3 more
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Likelihood‐based Inference with Missing Data Under Missing‐at‐Random
Scandinavian Journal of Statistics, 2015AbstractLikelihood‐based inference with missing data is challenging because the observed log likelihood is often an (intractable) integration over the missing data distribution, which also depends on the unknown parameter. Approximating the integral by Monte Carlo sampling does not necessarily lead to a valid likelihood over the entire parameter space ...
Yang, Shu, Kim, Jae Kwang
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