Fetching the paper…
Reading the bibliography…
In many application settings, the data have missing entries which make analysis challenging.
Nearest neighbor pattern classification
Thomas Cover and Peter Hart · 1967
Earlier work this paper cites.
Monte carlo sampling methods using markov chains and their applications
W Keith Hastings · 1970
Earlier work this paper cites.
Inference and missing data
Donald B Rubin · 1976
Earlier work this paper cites.
Maximum likelihood from incomplete data via the em algorithm
Arthur P Dempster, Nan M Laird, and Donald B Rubin · 1977
Earlier work this paper cites.
Classification and Regression Trees
Leo Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone · 1984
Earlier work this paper cites.
Reducing bias in observational studies using subclassification on the propensity score
Paul R Rosenbaum and Donald B Rubin · 1984
Earlier work this paper cites.
Multiple Imputation for Nonresponse in Surveys
D. B. Rubin · 1987
Earlier work this paper cites.
The strength of weak learnability
Robert E Schapire · 1990
Earlier work this paper cites.
Multivariate adaptive regression splines
Jerome H Friedman · 1991
Earlier work this paper cites.
Regression with missing x’s: a review
Roderick JA Little · 1992
Earlier work this paper cites.
Multiple-imputation inferences with uncongenial sources of input
Xiao-Li Meng · 1994
Earlier work this paper cites.
Support-vector networks
Corinna Cortes and Vladimir Vapnik · 1995
Earlier work this paper cites.
Bagging predictors
Leo Breiman · 1996
Earlier work this paper cites.
Indicator and stratification methods for missing explanatory variables in multiple linear regression
Michael P Jones · 1996
Earlier work this paper cites.
An introduction to recursive partitioning using the rpart routines, 1997
Terry M Therneau, Elizabeth J Atkinson, et al · 1997
Earlier work this paper cites.
An overview of statistical learning theory
Vladimir Naumovich Vapnik · 1999
Earlier work this paper cites.
Missing data , volume 136
Paul D Allison · 2001
Earlier work this paper cites.
Random forests
Leo Breiman · 2001
Earlier work this paper cites.
An analysis of four missing data treatment methods for supervised learning
Gustavo EAPA Batista and Maria Carolina Monard · 2003
Earlier work this paper cites.
Are loss functions all the same?
Lorenzo Rosasco, Ernesto De Vito, Andrea Caponnetto, Michele Piana, and Alessandro Verri · 2004
Earlier work this paper cites.
Pattern recognition and machine learning
Christopher M Bishop · 2006
Earlier work this paper cites.
Unbiased recursive partitioning: A conditional inference framework
Torsten Hothorn, Kurt Hornik, and Achim Zeileis · 2006
Earlier work this paper cites.
Handling missing values when applying classification models
Maytal Saar-Tsechansky and Foster Provost · 2007
Earlier work this paper cites.
Unbiased split selection for classification trees based on the gini index
C. Strobl, A.-L. Boulesteix, and T. Augustin · 2007
Cited alongside, same era.
Good methods for coping with missing data in decision trees
B. E. T. H. Twala, M. C. Jones, and D. J. Hand · 2008
Cited alongside, same era.
Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
Cited alongside, same era.
How should variable selection be performed with multiply imputed data?
Angela M. Wood, Ian R. White, and Patrick Royston · 2008
Cited alongside, same era.
Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls
Jonathan A C Sterne, Ian R White, John B Carlin, Michael Spratt, Patrick Royston, Michael G Kenward, Angela M Wood, and James R Carpenter · 2009
Cited alongside, same era.
mice: Multivariate imputation by chained equations in r
Dataset shift in machine learning
Masashi Sugiyama, Neil D Lawrence, Anton Schwaighofer, et al · 2017
Later among the works it cites.
When can multiple imputation improve regression estimates?
Vincent Arel-Bundock and Krzysztof J Pelc · 2018
Later among the works it cites.
From predictive methods to missing data imputation: an optimization approach
Dimitris Bertsimas, Colin Pawlowski, and Ying Daisy Zhuo · 2018
Later among the works it cites.
Mida: Multiple imputation using denoising autoencoders
Lovedeep Gondara and Ke Wang · 2018
Later among the works it cites.
Introduction to the special section on missing data
Julie Josse and Jerome P. Reiter · 2018
Later among the works it cites.
Graphical models for processing missing data
Karthika Mohan and Judea Pearl · 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S van Buuren and Karin Groothuis-Oudshoorn · 2010
Cited alongside, same era.
An investigation of missing data methods for classification trees applied to binary response data
Yufeng Ding and Jeffrey S Simonoff · 2010
Cited alongside, same era.
Scikit-learn: Machine Learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Cited alongside, same era.
Em algorithm and variants: An informal tutorial
Alexis Roche · 2011
Cited alongside, same era.
Missforest—non-parametric missing value imputation for mixed-type data
Daniel J Stekhoven and Peter Bühlmann · 2011
Cited alongside, same era.
A probabilistic theory of pattern recognition , volume 31
Luc Devroye, László Györfi, and Gábor Lugosi · 2013
Cited alongside, same era.
Package ‘norm’
John Fox · 2013
Cited alongside, same era.
Later among the works it cites.
Multiple imputation: A review of practical and theoretical findings
Jared S. Murray · 2018
Later among the works it cites.
Missing data imputation for supervised learning
Jason Poulos and Rafael Valle · 2018
Later among the works it cites.
R: A Language and Environment for Statistical Computing
R Core Team · 2018
Later among the works it cites.
rpart: Recursive Partitioning and Regression Trees , 2018
Terry Therneau and Beth Atkinson · 2018
Later among the works it cites.
Flexible Imputation of Missing Data
S. van Buuren · 2018
Later among the works it cites.
Gain: Missing data imputation using generative adversarial nets
Jinsung Yoon, James Jordon, and Mihaela Schaar · 2018
Later among the works it cites.
Logistic regression with missing covariates–parameter estimation, model selection and prediction
Wei Jiang, Julie Josse, and Marc Lavielle · 2019
Closest in time.
Misgan: Learning from incomplete data with generative adversarial networks
Steven Cheng-Xian Li, Bo Jiang, and Benjamin Marlin · 2019
Closest in time.
Statistical analysis with missing data , volume 793
Roderick JA Little and Donald B Rubin · 2019
Closest in time.
MIWAE: Deep generative modelling and imputation of incomplete data sets
Pierre-Alexandre Mattei and Jes Frellsen · 2019
Closest in time.
Linear predictor on linearly-generated data with missing values: non consistency and solutions
Marine Le Morvan, Nicolas Prost, Julie Josse, Erwan Scornet, and Gaël Varoquaux · 2020
Closest in time.
grf: Generalized Random Forests , 2020
Julie Tibshirani, Susan Athey, and Stefan Wager · 2020
Closest in time.
A benchmark for data imputation methods
Sebastian Jäger, Arndt Allhorn, and Felix Bießmann · 2021
Closest in time.
R-miss-tastic: a unified platform for missing values methods and workflows
Imke Mayer, Sportisse Aude, Nicholas Tierney, Nathalie Vialaneix, and Julie Josse · 2022
Closest in time.
Benchmarking missing-values approaches for predictive models on health databases
Alexandre Perez-Lebel, Gaël Varoquaux, Marine Le Morvan, Julie Josse, and Jean-Baptiste Poline · 2022
Closest in time.
Are deep learning models superior for missing data imputation in surveys? evidence from an empirical comparison
Zhenhua Wang, Olanrewaju Akande, Jason Poulos, and Fan Li · 2022
Closest in time.
Imputation scores
Jeffrey Näf, Meta-Lina Spohn, Loris Michel, and Nicolai Meinshausen · 2023
Closest in time.