R-miss-tastic

A resource website on missing values - Methods and references for managing missing data


A commented version of this bibliography can be found here.

Publication type Year Author
Citation Year Publication type
Abayomi, K., A. Gelman, and M. Levy. Diagnostics for multivariate imputations. In: Journal of the Royal Statistical Society, Series C (Applied Statistics) 57.3 (2008), pp. 273-291.
DOI
2008 Article
Albert, P. S. and D. A. Follmann. Modeling repeated count data subject to informative dropout. In: Biometrics 56.3 (2000), pp. 667-677.
DOI
2000 Article
Allison, P. D. Missing Data. Quantitative Applications in the Social Sciences. Thousand Oaks, CA, USA: Sage Publications, 2001. ISBN: 9780761916727.
DOI
2001 Book
Andridge, R. and R. J. A. Little. A review of hot deck imputation for survey non-response. In: International Statistical Review 78.1 (2010), pp. 40-64.
DOI
2010 Article
Audigier, V., F. Husson, and J. Josse. A principal component method to impute missing values for mixed data. In: Advances in Data Analysis and Classification 10.1 (2016), pp. 5-26.
DOI
2016 Article
Audigier, V., F. Husson, and J. Josse. MIMCA: multiple imputation for categorical variables with multiple correspondence analysis. In: Statistics and Computing 27.2 (2016), pp. 1-18. eprint: 1505.08116.
DOI
2016 Article
Audigier, V., F. Husson, and J. Josse. Multiple imputation for continuous variables using a Bayesian principal component analysis. In: Journal of Statistical Computation and Simulation 86.11 (2015), pp. 2140-2156.
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2015 Article
Bang, H. and J. M. Robins. Doubly robust estimation in missing data and causal inference models. In: Biometrics 61.4 (2005), pp. 962-973.
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2005 Article
Baraldi, A. N. and C. K. Enders. An introduction to modern missing data analysis. In: Journal of School Psychology 48.1 (2010), pp. 5-37.
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2010 Article
Baretta, L. and A. Santaniello. Nearest neighbor imputation algorithms: a critical evaluation. In: BMC Medical Informatics and Decision Making. Proceedings of the 5th Translational Bioinformatics Conference (TBC 2015): medical informatics and decision making 16.Supp. 3 (2016), p. 74.
DOI
2016 Article
Bartlett, J. W., O. Harel, and J. R. Carpenter. Asymptotically unbiased estimation of exposure odds ratios in complete records logistic regression. In: American journal of epidemiology 182.8 (2015), pp. 730–736.
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2015 Article
Bengio, Y. and F. Gingras. Recurrent neural networks for missing or asynchronous data. In: Proceedings of the 8th International Conference on Neural Information Processing Systems. (Nov. 27, 1995-Dec. 02, 1995). Ed. by -. Cambridge, MA, USA: MIT Press, 1995, pp. 395-401.
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1995 Paper
Biessmann, F., D. Salinas, S. Schelter, et al. “Deep” Learning for Missing Value Imputation in Tables with Non-Numerical Data. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management. Ed. by -. CIKM ’18. Torino, Italy: ACM, 2018, pp. 2017–2025. ISBN: 978-1-4503-6014-2. 2018 Paper
Blake, H. A., C. Leyrat, K. Mansfield, et al. Propensity scores using missingness pattern information: a practical guide. In: arXiv preprint (2019). arXiv: 1901.03981 [stat.ME].
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2019 Article
Buck, S. F. A method of estimation of missing values in multivariate data suitable for use with an electronic computer. In: Journal of the Royal Statistical Society, Series B 22 (1960), pp. 302-306.
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1960 Article
Burns, R. M. Multiple and replicate item imputation in a complex sample survey. In: Proceedings of the 6th Annual Research Conference. Ed. by B. of the Census. Washington DC, USA, 1990, pp. 655-665. 1990 Paper
Buuren, S. van. Flexible Imputation of Missing Data. Boca Raton, FL: Chapman and Hall/CRC, 2018.
URL
2018 Book
Buuren, S. van. Multiple imputation of discrete and continuous data by fully conditional specification. In: Statistical Methods in Medical Research 16 (2007), pp. 219-242.
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2007 Article
Buuren, S. van, J. P. L. Brand, C. G. M. Groothuis-Oudshoorn, et al. Fully conditional specification in multivariate imputation. In: Journal of Statistical Computation and Simulation 76.12 (2006), pp. 1049-1064.
DOI
2006 Article
Buuren, S. van and K. Groothuis-Oudshoorn. MICE: multivariate imputation by chained equations in R. In: Journal of Statistical Software 45 (2011), p. 3. eprint: NIHMS150003.
DOI
2011 Article
Candès, E. J., C. A. Sing-Long, and J. D. Trzasko. Unbiased risk estimates for singular value thresholding and spectral estimators. In: IEEE Transactions on Signal Processing 61.19 (2013), pp. 4643-4657.
DOI
2013 Article
Carpenter, J. R., M. G. Kenward, and S. Vansteelandt. A comparison of multiple imputation and doubly robust estimation for analyses with missing data. In: Journal of the Royal Statistical Society: Series A (Statistics in Society) 169.3 (2006), pp. 571–584.
DOI
2006 Article
Carpenter, J. and M. Kenward. Multiple Imputation and its Application. Chichester, West Sussex, UK: Wiley, 2013. ISBN: 9780470740521.
DOI
2013 Book
Chen, T. and C. Guestrin. XGBoost: A Scalable Tree Boosting System. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. (Aug. 13, 2016-Aug. 17, 2016). Ed. by -. New York, NY, USA: ACM, 2016, pp. 785-794. ISBN: 0450342322.
DOI
2016 Paper
Chen, J. and J. Shao. Nearest neighbor imputation for survey data. In: Journal of Official Statistics 16.2 (2000), pp. 113-131.
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2000 Article
Collins, L. M., J. L. Schafer, and K. Chi-Ming. A comparison of inclusive and restrictive strategies in modern missing data procedures. In: Psychological Methods 6.4 (2007), pp. 330-351.
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2007 Article
Cranmer, S. J. and J. Gill. We have to be discrete about this: a non-parametric imputation technique for missing categorical data. In: British Journal of Political Science 43 (2012), pp. 425-449.
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2012 Article
Crookston, N. L. and A. O. Finley. yaImpute: an R package for kNN imputation. In: Journal of Statistical Software 23 (2008), p. 10.
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2008 Article
Dax, A. Imputing Missing Entries of a Data Matrix: A review. In: Journal of Advanced Computing 3.3 (2014), pp. 98-222.
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2014 Article
Dempster, A. P., N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. In: Journal of the Royal Statistical Society, Series B (Methodological) 39.1 (1977), pp. 1-38.
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1977 Article
Diggle, P. and M. G. Kenward. Informative drop-out in longitudinal data analysis. In: Journal of the Royal Statistical Society, Series C (Applied Statistics) 43.1 (1994), pp. 49-93.
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1994 Article
Ding, P. and F. Li. Causal Inference: A Missing Data Perspective. In: Statistical Science 33.2 (2018), pp. 214–237.
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2018 Article
Ding, Y. and J. S. Simonoff. An investigation of missing data methods for classification trees applied to binary response data. In: Journal of Machine Learning Research 11.1 (2010), pp. 131-170.
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2010 Article
Dong, Y. and C. J. Peng. Principled missing data methods for researchers. In: SpringerPlus 2 (2013), p. 222.
DOI
2013 Article
Enders, C. K. Applied Missing Data Analysis. Guilford Press, 2010, p. 401. ISBN: 9781606236390. 2010 Book
Enders, C. K. A primer on maximum likelihood algorithms available for use with missing data. In: Structural Equation Modeling 8.1 (2001), pp. 128-141.
DOI
2001 Article
Fang, F., J. Zhao, and J. Shao. Imputation-based adjusted score equations in generalized linear models with nonignorable missing covariate values. In: Statistica Sinica 28.4 (2018), pp. 1677–1701.
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2018 Article
Fay, R. E. Alternative paradigms for the analysis of imputed survey data. In: Journal of the American Statistical Association 91.434 (1996), pp. 490-498.
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1996 Article
Fellegi, I. P. and D. Holt. A systematic approach to automatic edit and imputation. In: Journal of the American Statistical Association 71.353 (1976), pp. 17-35.
DOI
1976 Article
Ferrari, P. A., P. Annoni, A. Barbiero, et al. An imputation method for categorical variables with application to nonlinear principal component analysis. In: Computational Statistics & Data Analysis 55.7 (2011), pp. 2410-2420.
DOI
2011 Article
Finkbeiner, C. Estimation for the multiple factor model when data are missing. In: Psychometrika 44.4 (1979), pp. 409-420.
DOI
1979 Article
Fitzmaurice, G. M., G. Molenberghs, and S. R. Lipsitz. Regression Models for Longitudinal Binary Responses with Informative Drop-Outs. In: Journal of the Royal Statistical Society. Series B (Methodological) 57.4 (1995), pp. 691–704.
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1995 Article
Follmann, D. and M. Wu. An approximate generalized linear model with random effects for informative missing data. In: Biometrics 51.1 (1995), pp. 151-168.
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1995 Article
Gad, A. M. and N. M. M. Darwish. A shared parameter model for longitudinal data with missing values. In: American Journal of Applied Mathematics and Statistics 1.2 (2013), pp. 30-35.
URL
2013 Article
Gelman, A., G. King, and C. Liu. Not asked and not answered: Multiple imputation for multiple surveys. In: Journal of the American Statistical Association 93.443 (1998), pp. 846–857.
DOI
1998 Article
Gelman, A., I. van Mechelen, G. Verbeke, et al. Multiple Imputation for Model Checking: Completed-Data Plots with Missing and Latent Data. In: Biometrics 61.1 (2005), pp. 74–85.
DOI
2005 Article
Gill, R. D., M. J. Van Der Laan, and J. M. Robins. Coarsening at random: Characterizations, conjectures, counter-examples. In: Proceedings of the First Seattle Symposium in Biostatistics. Springer. 1997, pp. 255–294.
DOI
1997 Paper
Gondara, L. and K. Wang. MIDA: Multiple Imputation using Denoising Autoencoders. In: Proceedings of the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2018). (Jun. 03, 2018-Jun. 06, 2018). Ed. by D. Phung, V. Tseng, G. Webb, B. Ho, M. Ganji and L. Rashidi. Lecture Notes in Computer Science. Springer International Publishing, 2018, pp. 260-272. ISBN: 3319930404. 2018 Paper
Goodfellow, I., M. Mirza, A. Courville, et al. Multi-Prediction Deep Boltzmann Machines. In: Proceedings of the 26th International Conference on Neural Information Processing Systems. (Dec. 05, 2013-Dec. 10, 2013). Ed. by C. Burges, L. Bottou, M. Welling, Z. Ghahramani and K. Weinberger. Advances in Neural Information Processing Systems 26. Curran Associates, Inc., 2013, pp. 548–556.
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2013 Paper
Graham, J. W. Missing data analysis: making it work in the real world. In: Annual Review of Psychology 60 (2009), pp. 549-576.
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2009 Article
Graham, J. W., S. M. Hofer, S. I. Donaldson, et al. The Science of Prevention: Methodological Advances from Alcohol and Substance Abuse Research. In: The Science of Prevention: Methodological Advances from Alcohol and Substance Abuse Research. Ed. by K. Bryant, M. Windle and S. West. Washington, DC, USA: American Psychological Association, 1997. Chap. Analysis with missing data in prevention research, pp. 325-366. ISBN: 1-55798-439-5.
DOI
1997 Book
Graham, J. W., A. E. Olchowski, and T. E. Gilreath. How many imputations are really needed? Some practical clarifications of multiple imputation theory. In: Prevention Science 8.3 (2007), pp. 206-213.
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2007 Article
Heckman, J. Sample selection bias as a specification error. In: Econometrica 47.1 (1979), pp. 153-161.
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1979 Article
Heckman, J. J. The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models. In: Annals of Economic and Social Measurement 5.4 (1976), pp. 475-492.
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1976 Article
Hogan, J. W. and N. M. Laird. Mixture models for the joint distribution of repeated measures and event times. In: Statistics in Medecine 16.1-3 (1997), pp. 239-257. 1997 Article
Hogan, J. W. and T. Lancaster. Instrumental variables and inverse probability weighting for causal inference from longitudinal observational studies. In: Statistical Methods in Medical Research 13.1 (2004), pp. 17-48.
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2004 Article
Honaker, J., G. King, and M. Blackwell. Amelia II: a program for missing data. In: Journal of Statistical Software 45.7 (2011). eprint: arXiv:1501.0228.
DOI
2011 Article
Hothorn, T., K. Hornik, and A. Zeileis. Unbiased Recursive Partitioning: A Conditional Inference Framework. In: Journal of Computational and Graphical Statistics 15.3 (2012), pp. 651-674.
DOI
2012 Article
Horton, N. J. and K. P. Kleinman. Much Ado About Nothing - A Comparison of Missing Data Methods and Software to Fit Incomplete Data Regression Models. In: The American Statistician 61.1 (2017), pp. 79-90.
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2017 Article
Huisman, M. Imputation of missing item responses: some simple techniques. In: Quality & Quantity 34.4 (2000), pp. 331-351.
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2000 Article
Husson, F. and J. Josse. Handling missing values in multiple factor analysis. In: Food Quality and Preference 30 (2013), pp. 77-85.
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2013 Article
Ibrahim, J. G., S. R. Lipsitz, and M. Chen. Missing Covariates in Generalized Linear Models When the Missing Data Mechanism is Non-Ignorable. In: Journal of the Royal Statistical Society. Series B (Statistical Methodology) 61.1 (1999), pp. 173-190. 1999 Article
Ibrahim, J. G., M. Chen, and S. R. Lipsitz. Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable. In: Biometrika 88.2 (2001), pp. 551-564.
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2001 Article
Ilin, A. and T. Raiko. Practical approaches to Principal Component Analysis in the presence of missing values. In: Journal of Machine Learning Research 11 (2010), pp. 1957-2000.
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2010 Article
Imbert, A., A. Valsesia, C. Le Gall, et al. Multiple hot-deck imputation for network inference from RNA sequencing data. In: Bioinformatics 34.10 (2018), pp. 1726-1732.
DOI
2018 Article
Jönsson, P. and C. Wohlin. An evaluation of k-nearest neighbour imputation using lIkert data. In: Proceedings of the 10th International Symposium on Software Metrics. (Sep. 14, 2004-Sep. 16, 2004). Ed. by -. Chicago, IL, USA: IEEE, 2004, pp. 1530-1435. ISBN: 0769521290.
DOI
2004 Paper
Jamshidian, M. and S. Jalal. Tests of homoscedasticity, normality, and missing completely at random for incomplete multivariate data. In: Psychometrika 75.4 (2010), pp. 649-674. eprint: NIHMS150003.
DOI
2010 Article
Jamshidian, M., S. Jalal, and C. Jansen. MissMech: an R package for testing homoscedasticity, multivariate normality, and missing completely at random (MCAR). In: Journal of Statistical Software 56.6 (2014), pp. 1-31.
DOI
2014 Article
Jiang, W., J. Josse, and M. Lavielle. Logistic Regression with Missing Covariates–Parameter Estimation, Model Selection and Prediction. In: arXiv preprint (2018). arXiv: 1805.04602 [stat.ME]. 2018 Article
Joenssen, D. W. and U. Bankhofer. Donor limited hot deck imputation: effect on parameter estimation. In: Journal of Theoretical and Applied Computer Science 6.3 (2012), pp. 58-70.
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2012 Article
Jones, M. P. Indicator and Stratification Methods for Missing Explanatory Variables in Multiple Linear Regression. In: Journal of the American Statistical Association 91.433 (1996), pp. 222-230.
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1996 Article
Josse, J., M. Chavent, B. Liquet, et al. Handling missing values with regularized iterative multiple correspondance analysis. In: Journal of Classification 29.1 (2012), pp. 91-116.
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2012 Article
Josse, J. and F. Husson. missMDA: a package for handling missing values in multivariate data analysis. In: Journal of Statistical Software 70.1 (2016), pp. 1-31.
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2016 Article
Josse, J. and F. Husson. Handling missing values in exploratory multivariate data analysis methods. In: Journal de la Société Française de Statistique 153.2 (2012), pp. 79-99.
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2012 Article
Josse, J., F. Husson, and J. Pagès. Gestion des données manquantes en Analyse en Composantes Principales. In: Journal de la Société Française de Statistique 150.2 (2009), pp. 28-51.
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2009 Article
Josse, J., J. Pagès, and F. Husson. Multiple imputation in principal component analysis. In: Advances in Data Analysis and Classification 5.3 (2011), pp. 231-246.
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2011 Article
Josse, J., N. Prost, E. Scornet, et al. On the consistency of supervised learning with missing values. In: arXiv preprint (2019). arXiv: 1902.06931 [stat.ML].
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2019 Article
Kaiser, J. Dealing with missing values in data. In: Journal of Systems Integration 5.1 (2014), pp. 42-51.
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2014 Article
Kallus, N., X. Mao, and M. Udell. Causal Inference with Noisy and Missing Covariates via Matrix Factorization. In: Advances in Neural Information Processing Systems. Ed. by -. 2018. eprint: 1806.00811.
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2018 Paper
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1986 Article
Kapelner, A. and J. Bleich. Prediction with missing data via Bayesian additive regression trees. In: Canadian Journal of Statistics 43.2 (2015), pp. 224-239. 2015 Article
Kim, J. K. and J. Shao. Statistical Methods for Handling Incomplete Data. Boca Raton, FL, USA: Chapman and Hall/CRC, 2013. ISBN: 9781482205077. 2013 Book
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1986 Article
Kowarik, A. and M. Templ. Imputation with the R Package VIM. In: Journal of Statistical Software 74.7 (2016), pp. 1-16.
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2016 Article
Kropko, J., B. Goodrich, A. Gelman, et al. Multiple Imputation for Continuous and Categorical Data: Comparing Joint Multivariate Normal and Conditional Approaches. In: Political Analysis 22.4 (2014), pp. 497–519.
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2014 Article
Lee, K. M., R. Mitra, and S. Biedermann. Optimal design when outcome values are not missing at random. In: Statistica Sinica 28.4 (2018), pp. 1821–1838.
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2018 Article
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1988 Article
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Mohan, K. and J. Pearl. Graphical Models for Processing Missing Data. Tech. rep. R-473-L. Forthcoming, Journal of American Statistical Association (JASA). CA: Department of Computer Science, University of California, Los Angeles, 2019.
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Molenberghs, G. and M. G. Kenward. Missing Data in Clinical Studies. Chichester, West Sussex, UK: Wiley, 2007. ISBN: 9780470849811.
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