MICE

R package mice: Multivariate Imputation by Chained Equations. Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm. Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.


References in zbMATH (referenced in 109 articles , 1 standard article )

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  1. Bishoyi, Abhishek; Wang, Xiaojing; Dey, Dipak K.: Learning semiparametric regression with missing covariates using Gaussian process models (2020)
  2. Jiang, Wei; Josse, Julie; Lavielle, Marc; TraumaBase Group: Logistic regression with missing covariates -- parameter estimation, model selection and prediction within a joint-modeling framework (2020)
  3. Cui, Ruifei; Bucur, Ioan Gabriel; Groot, Perry; Heskes, Tom: A novel Bayesian approach for latent variable modeling from mixed data with missing values (2019)
  4. Diallo, Alpha Oumar; Diop, Aliou; Dupuy, Jean-François: Estimation in zero-inflated binomial regression with missing covariates (2019)
  5. Hadrien Lorenzo, Jérôme Saracco, Rodolphe Thiébaut: Supervised Learning for Multi-Block Incomplete Data (2019) arXiv
  6. Jorge Cimentada: perccalc: An R package for estimating percentiles from categorical variables (2019) not zbMATH
  7. Kocheturov, Anton; Pardalos, Panos M.; Karakitsiou, Athanasia: Massive datasets and machine learning for computational biomedicine: trends and challenges (2019)
  8. Liu, Lin; Qiu, Yuqi; Natarajan, Loki; Messer, Karen: Imputation and post-selection inference in models with missing data: an application to colorectal cancer surveillance guidelines (2019)
  9. Parrella, Maria Lucia; Albano, Giuseppina; La Rocca, Michele; Perna, Cira: Reconstructing missing data sequences in multivariate time series: an application to environmental data (2019)
  10. Quartagno, Matteo; Carpenter, James R.: Multiple imputation for discrete data: evaluation of the joint latent normal model (2019)
  11. Rabin, Neta; Fishelov, Dalia: Two directional Laplacian pyramids with application to data imputation (2019)
  12. Ramosaj, Burim; Pauly, Markus: Predicting missing values: a comparative study on non-parametric approaches for imputation (2019)
  13. Roy, Arkaprava; Ghosal, Subhashis; Prescott, Jeffrey; Choudhury, Kingshuk Roy: Bayesian modeling of the structural connectome for studying Alzheimer’s disease (2019)
  14. Teter, Michael D.; Royset, Johannes O.; Newman, Alexandra M.: Modeling uncertainty of expert elicitation for use in risk-based optimization (2019)
  15. Thao, Le Thi Phuong; Geskus, Ronald: A comparison of model selection methods for prediction in the presence of multiply imputed data (2019)
  16. Uiwon Hwang, Dahuin Jung, Sungroh Yoon: HexaGAN: Generative Adversarial Nets for Real World Classification (2019) arXiv
  17. Ardakani, Omid M.; Kishor, N. Kundan; Song, Suyong: Re-evaluating the effectiveness of inflation targeting (2018)
  18. Audigier, Vincent; White, Ian R.; Jolani, Shahab; Debray, Thomas P. A.; Quartagno, Matteo; Carpenter, James; van Buuren, Stef; Resche-Rigon, Matthieu: Multiple imputation for multilevel data with continuous and binary variables (2018)
  19. Bertsimas, Dimitris; Pawlowski, Colin; Zhuo, Ying Daisy: From predictive methods to missing data imputation: an optimization approach (2018)
  20. Geraci, Marco; McLain, Alexander: Multiple imputation for bounded variables (2018)

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