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Missing data
Known as:
MCAR
, Missingness
, Missing at random
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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…
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Related topics
Related topics
6 relations
Censoring (statistics)
Data mining
Interpolation
Markov chain
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Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2012
Highly Cited
2012
Analysis of Missing Data
J. Graham
2012
Corpus ID: 116438243
In this chapter, I present older methods for handling missing data. I then turn to the major new approaches for handling missing…
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Review
2009
Review
2009
PPCA-Based Missing Data Imputation for Traffic Flow Volume: A Systematical Approach
Li Qu
,
Jianming Hu
,
Li Li
,
Yi Zhang
IEEE transactions on intelligent transportation…
2009
Corpus ID: 3486685
The missing data problem greatly affects traffic analysis. In this paper, we put forward a new reliable method called…
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Highly Cited
2009
Highly Cited
2009
Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis by DANIELS, M. J. and HOGAN, J. W
A. Rotnitzky
,
D. Heitjan
,
M. Gomes
2009
Corpus ID: 44027425
In most studies, the intended (full) data, i.e., the data that the study investigators wish to collect, are inevitably…
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Highly Cited
2009
Highly Cited
2009
Computational Intelligence for Missing Data Imputation, Estimation, and Management - Knowledge Optimization Techniques
T. Marwala
Computational Intelligence for Missing Data…
2009
Corpus ID: 108025692
In recent years, the issue of missing data imputation has been extensively explored in information engineering. Computational…
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Review
2009
Review
2009
Statistical Power Analysis with Missing Data: A Structural Equation Modeling Approach
A. Davey
,
J. Savla
2009
Corpus ID: 117360864
1. Introduction. Part 1. Fundamentals. 2. The LISREL Model. 3. Missing Data: An Overview. 4. Estimating Statistical Power with…
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Highly Cited
2004
Highly Cited
2004
Towards Missing Data Imputation: A Study of Fuzzy K-means Clustering Method
Dan Li
,
J. Deogun
,
William Spaulding
,
B. Shuart
Rough Sets and Current Trends in Computing
2004
Corpus ID: 6769023
In this paper, we present a missing data imputation method based on one of the most popular techniques in Knowledge Discovery in…
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Highly Cited
2003
Highly Cited
2003
Adding Missing-Data-Relevant Variables to FIML-Based Structural Equation Models
J. Graham
2003
Corpus ID: 54602934
Conventional wisdom in missing data research dictates adding variables to the missing data model when those variables are…
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Highly Cited
2003
Highly Cited
2003
Multiple Imputation for Missing Data: Making the most of What you Know
Mark Fichman
,
Jonathon N. Cummings
2003
Corpus ID: 2574680
Missing data are a common problem in organizational research. Missing data can occur due to attrition in a longitudinal study or…
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Highly Cited
2001
Highly Cited
2001
Singular spectrum analysis for time series with missing data
D. Schoellhamer
2001
Corpus ID: 17851060
Geophysical time series often contain missing data, which prevents analysis with many signal processing and multivariate tools. A…
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Highly Cited
1999
Highly Cited
1999
Imputation of Missing Data in Industrial Databases
K. Lakshminarayan
,
S. Harp
,
T. Samad
Applied intelligence (Boston)
1999
Corpus ID: 14422178
A limiting factor for the application of IDA methods in many domains is the incompleteness of data repositories. Many records…
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