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On the Joys of Missing Data [PDF]
Journal of Pediatric Psychology, 2013 We provide conceptual introductions to missingness mechanisms--missing completely at random, missing at random, and missing not at random--and state-of-the-art methods of handling missing data--full-information maximum likelihood and multiple imputation--followed by a discussion of planned missing designs: Multiform questionnaire protocols, 2-method ...
Little, Todd D. +3 more
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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
doaj +2 more sources
Testing Measurement Invariance with Ordinal Missing Data: A Comparison of Estimators and Missing Data Techniques [PDF]
, 2019 Ordinal missing data are common in measurement equivalence/invariance (ME/I) testing studies. However, there is a lack of guidance on the appropriate method to deal with ordinal missing data in ME/I testing.
Chen, Po-Yi +4 more
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A new weighted NMF algorithm for missing data interpolation and its application to speech enhancement [PDF]
, 2019 In this paper we present a novel weighted NMF (WNMF) algorithm for interpolating missing data. The proposed approach has a computational cost equivalent to that of standard NMF and, additionally, has the flexibility to control the degree of interpolation
Gangashetty, S. +2 more
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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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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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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
doaj +1 more source
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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Clustering of Data with Missing Entries
, 2018 The analysis of large datasets is often complicated by the presence of missing entries, mainly because most of the current machine learning algorithms are designed to work with full data. The main focus of this work is to introduce a clustering algorithm,
Jacob, Mathews, Poddar, Sunrita
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National Estimates of Children Missing Involuntarily or for Benign Reasons. [PDF]
, 2005 Provides information on the numbers and characteristics of two groups not often recognized in the literature on missing children: children involuntarily missing because they were lost or injured and those missing because of a benign explanation such as a
Finkelhor, David +2 more
core +2 more sources