Multiple Imputation For Nonresponse In Surveys

2016

6,112 citations recorded by the source corpus.

Subfields: Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/3473ba4ba7bb59b24557fe073a36c52d68db9a77

Citations in the corpus

• Prevention of Cyberbullying and Cyber Victimization: Evaluation of the ViSC Social Competence Program (2015, positive): “Multiple imputation (Rubin, 1987) under the missing at random assumption was used to deal with missing data.”
• Efficient Quantile Regression Analysis With Missing Observations (2015, neutral): “Multiple imputation (Rubin 1987; Lipsitz et al. 1998; Aerts et al. 2002) is also widely used for remediating problems caused by incomplete data.”
• Bias-Reduced Doubly Robust Estimation (2015, neutral): “We assume that missingness is explainable by X , a collection of auxiliary covariates, so that the missing at random (MAR, Rubin (1976)) assumption holds: Y ⊥⊥ R|X .”
• Dealing With Omitted and Not-Reached Items in Competence Tests (2014, neutral): “…include corrected mean substitution (Bernaards & Sijtsma, 2000; Pohl et al. 425 Huisman & Molenaar, 2001; Sijtsma & van der Ark, 2003), response function imputation (Sijtsma & van der Ark, 2003), expectation-maximization algorithm (Little & Rubin, 2002), and multiple imputation (MI; Rubin, 1987).”
• A novel clinical risk prediction model for sudden cardiac death in hypertrophic cardiomyopathy (HCM risk-SCD). (2014, positive): “A total of 25 imputed data sets were generated and the estimates were combined using Rubin’s rules.(38) Patientswith .”
• Predicting Academic Achievement and Attainment: The Contribution of Early Academic Skills, Attention Difficulties, and Social Competence (2016, neutral): “The model described in the next section was estimated for each imputed dataset and the results were combined (Rubin, 1987).”
• Combining non‐probability and probability survey samples through mass imputation (2018, positive): “One research topic is to explore the use of random imputation, including multiple imputation (Rubin, 1987) and fractional imputation (Kim, 2011), for analysing non- probability survey samples.”
• World income inequality databases: an assessment of WIID and SWIID (2015, positive): “The MI estimate of each country-year Gini coefficient is equal to the mean of the 100 imputed Ginis (see the discussion of Rubin’s Rules earlier). However, a conventional MI estimate of its standard error cannot be derived. Each country-year observation is a singleton, and the within-imputation vari”
• imputeTS: Time Series Missing Value Imputation in R (2017, neutral): “Most popular techniques like Multiple Imputation (Rubin, 1987), Expectation-Maximization (Dempster et al.”
• Shadow Economies Around the World: New Results for 158 Countries Over 1991-2015 (2017, positive): “In order to avoid the problems of calibrating the relative estimates of the MIMIC methodology we used a new method, the Predict Mean Matching one, developed by Rubin (1987).”
• Using Experiments to Correct for Selection in Observational Studies (2020, neutral): “There is an interesting connection between Assumptions 1-4 and the Missing At Random (MAR) assumption in the missing data literature (Rubin [1976], Little and Rubin [2019], Rubin [1987]).”
• GAIN: Missing Data Imputation using Generative Adversarial Nets (2018, positive): “Imputation algorithms are also used in many other applications such as image concealment, data compression, and counterfactual estimation (Rubin, 2004; Kreindler & Lumsden, 2012; Yoon et al., 2018a).”
• The Informal Economy in Sub-Saharan Africa: Size and Determinants (2017, positive): “Predictive Mean Matching uses survey-based observations of the size of the informal economy for 49 countries, and matches them to countries where data are missing through multiply-imputed datasets estimated by a linear regression.”