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INFERENCE AND MISSING DATA [PDF]
ETS Research Bulletin Series, 1975 ABSTRACTTwo results are presented concerning inference when data may be missing. First, ignoring the process that causes missing data when making sampling distribution inferences about the parameter of the data, θ, is generally appropriate if and only if the missing data are “missing at random” and the observed data are “observed at random,” and then ...
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Missing data imputation using classification and regression trees [PDF]
PeerJ Computer ScienceBackground Missing data are common when analyzing real data. One popular solution is to impute missing data so that one complete dataset can be obtained for subsequent data analysis.
Cheng-Yang Chen, Yu-Wei Chang
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DEGAIN: Generative-Adversarial-Network-Based Missing Data Imputation
Information, 2022 Insights and analysis are only as good as the available data. Data cleaning is one of the most important steps to create quality data decision making. Machine learning (ML) helps deal with data quickly, and to create error-free or limited-error datasets.
Reza Shahbazian, Irina Trubitsyna
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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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Missing Data Analysis in Regression
Applied Artificial Intelligence, 2022 Many of the datasets in real-world applications contain incompleteness. In this paper, we approach the effects and possible solutions to incomplete databases in regression, aiming to bridge a gap between theoretically effective algorithms.
C. G. Marcelino +3 more
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Identifying research priorities for effective retention strategies in clinical trials
Trials, 2017 Background The failure to retain patients or collect primary-outcome data is a common challenge for trials and reduces the statistical power and potentially introduces bias into the analysis. Identifying strategies to minimise missing data was the second
Anna Kearney +7 more
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Cognitive Diagnosis Modeling Incorporating Item-Level Missing Data Mechanism
Frontiers in Psychology, 2020 The aim of cognitive diagnosis is to classify respondents' mastery status of latent attributes from their responses on multiple items. Since respondents may answer some but not all items, item-level missing data often occur.
Na Shan +3 more
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Missing Categorical Data Imputation and Individual Observation Level Imputation
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2014 Traditional missing data techniques of imputation schemes focus on prediction of the missing value based on other observed values. In the case of continuous missing data the imputation of missing values often focuses on regression models.
Pavel Zimmermann +2 more
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Data-Driven Method for Missing Harmonic Data Completion
IEEE Access, 2021 In large-scale applications, missing harmonic data during transmission is inevitable. This paper presents a novel approach for the completion of missing harmonic data based on a data-driven approach.
Rui Xu +4 more
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A Tensor-Based Method for Completion of Missing Electromyography Data
IEEE Access, 2019 This paper discusses the recovery of missing data in surface electromyography (sEMG) signals that arise during the acquisition process. Missing values in the EMG signals occur due to either the disconnection of electrodes, artifacts and muscle fatigue or
Muhammad Akmal +4 more
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