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Wei Jiang's presentation on missing data analysis:
Imagine you are solving a linear regression but there are a lot of missing values in your design matrix, how can you deal with so many "NA"? The trouble may be caused by survey non-responses, lost records or machine failures. Classical packages performing estimation would let you ignore all the lines with missingness, as a result, much information would be lost. A more reasonable method to handle this problem, is modifying an estimation process so that the method can be applied to incomplete data. For example, one can use the Expectation-Maximization (EM) algorithm to obtain the maximum likelihood estimate despite missing values. Another popular choice is imputation, followed by classical regression procedure on imputed dataset. In this presentation, I will talk about different types of missing data, review several imputation methods and demonstrate how to estimate parameters based on EM algorithm with R.
Stochastic Variational Inference, Michal Kurtys
Suggested readings:
Books:
- Doing Bayesian Data Analysis, Second Edition: A Tutorial with R, JAGS, and Stan, John Kruschke https://www.amazon.com/Doing-Bayesian-Data-Analysis-Tutorial/dp/0124058884
- Bayesian Analysis with Python: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ, 2nd Edition, Osvaldo Martin. https://www.amazon.com/gp/product/1789341655/ref=dbs_a_def_rwt_bibl_vppi_i0
- Pattern Recognition andMachine Learning, Christopher M. Bishop http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf
Tutorials:
- https://twiecki.io/blog/2015/11/10/mcmc-sampling/
- https://pyro.ai/examples/intro_part_i.html
- https://www.shakirm.com/papers/VITutorial.pdf
Papers: Stochastic Variational Inference, http://www.columbia.edu/~jwp2128/Papers/HoffmanBleiWangPaisley2013.pdf