Copula selection models for non-Gaussian responses that are missing not at random [PDF]
, 2019 Missing not at random (MNAR) data poses key challenges for statistical inference because the model of interest is typically not identifiable without imposing further (e.g., distributional) assumptions. Sample selection models have been routinely used for
Camarena Brenes, J. +3 more
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Comparison of Different LGM-Based Methods with MAR and MNAR Dropout Data
Frontiers in Psychology, 2017 The missing not at random (MNAR) mechanism may bias parameter estimates and even distort study results. This study compared the maximum likelihood (ML) selection model based on missing at random (MAR) mechanism and the Diggle–Kenward selection model ...
Meijuan Li +5 more
doaj +1 more source
On Testing the Missing at Random Assumption [PDF]
, 2006 Most approaches to learning from incomplete data are based on the assumption that unobserved values are missing at random (mar). While the mar assumption, as such, is not testable, it can become testable in the context of other distributional assumptions, e.g. the naive Bayes assumption.
openaire +3 more sources
Doubly Robust Estimates for Binary Longitudinal Data Analysis with Missing Response and Missing Covariates [PDF]
, 2011 Longitudinal studies often feature incomplete response and covariate data. Likelihood-based methods such as the EM algorithm give consistent estimators for model parameters when data are missing at random provided that the response model and the missing
Chen, Baojiang, Zhou, Xiao-Hua
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Microdata Imputations and Macrodata Implications: Evidence from the Ifo Business Survey [PDF]
, 2012 A widespread method for now- and forecasting economic macro level parameters such as GDP growth rates are survey-based indicators which contain early information in contrast to official data.
Heumann, Christian, Seiler, Christian
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Analysis of Pregnancy and Other Factors on Detection of Human Papilloma Virus (HPV) Infection Using Weighted Estimating Equations for Follow-Up Data [PDF]
, 2000 Generalised estimating equations have been well established to draw inference for the marginal mean from follow-up data. Many studies suffer from missing data that may result in biased parameter estimates if the data are not missing completely at random.
Chang-Claude, J. +2 more
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Consequences of multiple imputation of missing standard deviations and sample sizes in meta‐analysis
Ecology and Evolution, 2020 Meta‐analyses often encounter studies with incompletely reported variance measures (e.g., standard deviation values) or sample sizes, both needed to conduct weighted meta‐analyses.
Stephan Kambach +5 more
doaj +1 more source
Journal of Clinical and Translational Science, 2021 Objectives/Goals: The association between surgery with general anesthesia (exposure) and cognition (outcome) among older adults has been studied with mixed conclusions.
Katrina L. Devick +4 more
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Inference for partial correlation when data are missing not at random
, 2017 We introduce uncertainty regions to perform inference on partial correlations when data are missing not at random. These uncertainty regions are shown to have a desired asymptotic coverage.
de Luna, Xavier, Gorbach, Tetiana
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Random Intersection Graphs and Missing Data
Proceedings of the AAAI Conference on Artificial Intelligence, 2020 Random-graphs and statistical inference with missing data are two separate topics that have been widely explored each in its field. In this paper we demonstrate the relationship between these two different topics and take a novel view of the data matrix as a random intersection graph.
Dror Salti, Yakir Berchenko
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