Randomly and Non-Randomly Missing Renal Function Data in the Strong Heart Study: A Comparison of Imputation Methods. [PDF]
PLoS ONE, 2015 Kidney and cardiovascular disease are widespread among populations with high prevalence of diabetes, such as American Indians participating in the Strong Heart Study (SHS).
Nawar Shara +7 more
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Evaluation of missing data mechanisms in two and three dimensional incomplete tables
, 2018 The analysis of incomplete contingency tables is a practical and an interesting problem. In this paper, we provide characterizations for the various missing mechanisms of a variable in terms of response and non-response odds for two and three dimensional
Ghosh, S., Vellaisamy, P.
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Multiple imputation with missing indicators as proxies for unmeasured variables: simulation study
BMC Medical Research Methodology, 2020 Background Within routinely collected health data, missing data for an individual might provide useful information in itself. This occurs, for example, in the case of electronic health records, where the presence or absence of data is informative.
Matthew Sperrin, Glen P. Martin
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Evaluation of Multiple Imputation with Large Proportions of Missing Data: How Much Is Too Much?
Iranian Journal of Public Health, 2021 Background: Multiple Imputation (MI) is known as an effective method for handling missing data in public health research. However, it is not clear that the method will be effective when the data contain a high percentage of missing observations on a ...
Jin Hyuk Lee, J. Charles Huber Jr.
doaj +1 more source
Variational Inference for Stochastic Block Models from Sampled Data
, 2019 This paper deals with non-observed dyads during the sampling of a network and consecutive issues in the inference of the Stochastic Block Model (SBM).
Barbillon, Pierre +2 more
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Collaborative Filtering and the Missing at Random Assumption
CoRR, 2012 Rating prediction is an important application, and a popular research topic in collaborative filtering. However, both the validity of learning algorithms, and the validity of standard testing procedures rest on the assumption that missing ratings are missing at random (MAR).
Benjamin M. Marlin +3 more
openaire +3 more sources
Variational Bayesian Inference for Quantile Regression Models with Nonignorable Missing Data
Mathematics, 2023 Quantile regression models are remarkable structures for conducting regression analyses when the data are subject to missingness. Missing values occur because of various factors like missing completely at random, missing at random, or missing not at ...
Xiaoning Li, Mulati Tuerde, Xijian Hu
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Missing at Random (MAR) in Nonparametric Regression - A Simulation Experiment [PDF]
, 2002 This paper considers an additive model y = f(x) + e when some observations on x are missing at random but corresponding observations on y are available.
Nittner, T.
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Families of children with cancer experience significant financial strain, even with universal healthcare. Indirect costs, such as productivity losses and non‐medical expenses, are rarely included in economic evaluations, and little is known about how effectively financial aid programmes alleviate this burden. Childhood brain tumours
Megumi Lim +8 more
wiley +1 more source
Heckman imputation models for binary or continuous MNAR outcomes and MAR predictors
BMC Medical Research Methodology, 2018 Background Multiple imputation by chained equations (MICE) requires specifying a suitable conditional imputation model for each incomplete variable and then iteratively imputes the missing values. In the presence of missing not at random (MNAR) outcomes,
Jacques-Emmanuel Galimard +3 more
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