Missing data

Imputation, estimation despite etc

October 7, 2021 — October 7, 2021

hidden variables
hierarchical models
machine learning
Figure 1

Placeholder to remind me to read Morvan et al. (2021).

1 References

Blackwell, Honaker, and King. 2015. A Unified Approach to Measurement Error and Missing Data: Details and Extensions.” Sociological Methods & Research.
Clark, and Bjørnstad. 2004. Population Time Series: Process Variability, Observation Errors, Missing Values, Lags, and Hidden States.” Ecology.
Dempster, Laird, and Rubin. 1977. Maximum Likelihood from Incomplete Data Via the EM Algorithm.” Journal of the Royal Statistical Society: Series B (Methodological).
Kennedy, Mauro, Daniels, et al. 2019. Handling Missing Data in Instrumental Variable Methods for Causal Inference.” Annual Review of Statistics and Its Application.
Mohan, and Pearl. 2018. Consistent Estimation Given Missing Data.” In International Conference on Probabilistic Graphical Models.
Mohan, Pearl, and Tian. 2013. Graphical Models for Inference with Missing Data.” In Advances in Neural Information Processing Systems.
Morvan, Josse, Scornet, et al. 2021. What’s a Good Imputation to Predict with Missing Values? arXiv:2106.00311 [Cs, Stat].
Rubin. 1987. The Calculation of Posterior Distributions by Data Augmentation: Comment: A Noniterative Sampling/Importance Resampling Alternative to the Data Augmentation Algorithm for Creating a Few Imputations When Fractions of Missing Information Are Modest: The SIR Algorithm.” Journal of the American Statistical Association.
Shpitser, Mohan, and Pearl. 2015. Missing Data as a Causal and Probabilistic Problem.”
Tu, Zhang, Ackermann, et al. 2018. Causal Discovery in the Presence of Missing Data.” arXiv:1807.04010 [Cs, Stat].