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Small area statistics from survey and imputed data [PDF]

Statistical Journal of the United Nations Economic Commission for Europe, 1999
This paper discusses improvement of statistics for small areas and groups utilizing sample survey data and data imputed by intensive use of background data from available census or administrative register sources. Reliable accuracy prediction for such statistics is important and is also discussed and investigated in the paper.
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Missing Data Handling via EM and Multiple Imputation in Network Analysis using Glasso and Atan Regularization

Multivariate Behavioral Research
The existing literature on missing data handling in psychological network analysis using cross-sectional data is currently limited to likelihood based approaches.
Kai Jannik Nehler, Martin Schultze
semanticscholar   +1 more source

Statistically Evaluating the Imputation Accuracy of Highway Agencies

Applications of Advanced Technologies in Transportation Engineering (2004), 2004
Data imputation has been practiced by traffic engineers since traffic data programs were established in the 1930s. However, a literature review indicates that no research has been conducted to assess imputation accuracy. An examination indicates that current practices are varied and intuitive in nature. Typical methods used by highway agencies are thus
Ming Zhong, Satish Sharma
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Introduction to Statistical Imputation

2018
Chapters 11 and 12 are concerned with imputation methods and tools. The first of them gives an introduction that explains the term itself and looks again at missingness issues that have been considered already. Now the main concern is whether to use imputation.
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A Multi-Step Fuzzy C-Means Approach for Accurate Data Imputation in Healthcare

2024 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI)
In this emerging technological era, data is the new oil. For a long time, missing values in data posed a huge challenge to machine learning, data statistics, data mining and other data-driven fields.
Subhashish Nayak, Swayam Smruti Dash, P. M. Khilar   +2 more
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Counterfactual Imputation: Comments on “Imputation of Counterfactual Outcomes when the Errors are Predictable” by Silvia Gonçalves and Serena Ng

Journal of Business & Economic Statistics
The measurement of treatment (intervention) effects on a single (or just a few) treated unit(s) based on counterfactuals constructed from artificial controls has become a popular practice in applied statistics and economics since the proposal of the ...
Marcelo C. Medeiros
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Multiple Imputation for Nonresponse and Statistical Disclosure Control

2011
Most if not all surveys are subject to item nonresponse, and even registers can contain missing values, if implausible values are set to missing during the data-editing process. Since the generation of synthetic datasets is based on the ideas of multiple imputation, it is reasonable to use the approach to impute missing values and generate synthetic ...
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The production of official agricultural statistics in 2040: What does the future hold?

Statistical Journal of the IAOS
Many National Statistical Offices are modernizing the systems and processes underpinning the production of official agricultural statistics. Moving data and processes to the cloud, collecting survey data via the web, automating editing and imputation ...
Linda J. Young, G. Carletto, Graciela Márquez, Dominik A. Rozkrut, Spiro Stefanou   +4 more
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A multistage deep imputation framework for missing values large segment imputation with statistical metrics

Applied Soft Computing, 2023
JinSheng Yang, YuanHai Shao, ChunNa Li, WenSi Wang   +3 more
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Multiple imputation using chained equations: Issues and guidance for practice

Statistics in Medicine, 2011
I. White, P. Royston, A. Wood
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