Results 31 to 40 of about 549,387 (258)
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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Multiple imputation of ordinal missing not at random data
AStA Advances in Statistical Analysis, 2022 AbstractWe introduce a selection model-based imputation approach to be used within the Fully Conditional Specification (FCS) framework for the Multiple Imputation (MI) of incomplete ordinal variables that are supposed to be Missing Not at Random (MNAR). Thereby, we generalise previous work on this topic which involved binary single-level and multilevel
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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.
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Deep Generative Imputation Model for Missing Not At Random Data
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023 Data analysis usually suffers from the Missing Not At Random (MNAR) problem, where the cause of the value missing is not fully observed. Compared to the naive Missing Completely At Random (MCAR) problem, it is more in line with the realistic scenario whereas more complex and challenging.
Jialei Chen +3 more
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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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Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback
, 2020 Counterfactual learning for dealing with missing-not-at-random data (MNAR) is an intriguing topic in the recommendation literature since MNAR data are ubiquitous in modern recommender systems. Missing-at-random (MAR) data, namely randomized controlled trials (RCTs), are usually required by most previous counterfactual learning methods for debiasing ...
Zifeng W +5 more
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Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback [PDF]
Proceedings of the 13th International Conference on Web Search and Data Mining, 2020 Recommender systems widely use implicit feedback such as click data because of its general availability. Although the presence of clicks signals the users' preference to some extent, the lack of such clicks does not necessarily indicate a negative response from the users, as it is possible that the users were not exposed to the items (positive ...
Saito, Yuta +4 more
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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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BMC Medical Research Methodology, 2023 Background Detecting treatment effect heterogeneity is an important objective in cluster randomized trials and implementation research. While sample size procedures for testing the average treatment effect accounting for participant attrition assuming ...
Jiaqi Tong +3 more
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