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Missing data

Known as: MCAR, Missingness, Missing at random 
In statistics, missing data, or missing values, occur when no data value is stored for the variable in an observation. Missing data are a common… Expand
Wikipedia

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2019
Review
2019
Since most social science research relies on multiple data sources, merging data sets is an essential part of researchers… Expand
Review
2019
Review
2019
The k‐nearest neighbors algorithm is characterized as a simple yet effective data mining technique. The main drawback of this… Expand
Review
2018
Review
2018
Adequate statistical power contributes to observing true relationships in a dataset. With a thoughtful power analysis, the… Expand
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Review
2017
Review
2017
Inferring causal effects of treatments is a central goal in many disciplines. The potential outcomes framework is a main… Expand
Highly Cited
2016
Highly Cited
2016
 
Highly Cited
2011
Highly Cited
2011
Amelia II is a complete R package for multiple imputation of missing data. The package implements a new expectation-maximization… Expand
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Highly Cited
2007
Highly Cited
2007
Part 1. A Gentle Introduction to Missing Data. The Concept of Missing Data. The Prevalence of Missing Data. Why Data Might Be… Expand
Highly Cited
2001
Highly Cited
2001
A Monte Carlo simulation examined the performance of 4 missing data methods in structural equation models: full information… Expand
Highly Cited
2000
Highly Cited
2000
Two algorithms for producing multiple imputations for missing data are evaluated with simulated data. Software using a propensity… Expand
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Highly Cited
1994
Highly Cited
1994
Abstract For missing data problems, Tanner and Wong have described a data augmentation procedure that approximates the actual… Expand
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