Skip to content
 

My suggested project for the MIT Better Science Ideathon: assessing the reasonableness of missing-data imputations.

Leo Celi writes:

We are 3 months away from the MIT Better Science Ideathon on April 23.

We would like to request your help with mentoring a team or 2 during the ideathon. During the ideathon, teams discuss a specific issue (lack of focus on reproducibility across majority of journals) or problem that arose from a well-intentioned initiative (e.g., proliferation of open-access journals), brainstorm on how to address the issue/problem and design a strategy. In this regard, we are soliciting ideas that the teams can choose from for the ideathon.

I’ll be participating in this event, and here’s my suggested project: assessing the reasonableness of missing-data imputations. Here are 3 things to read:
http://www.stat.columbia.edu/~gelman/research/published/paper77notblind.pdf
http://www.stat.columbia.edu/~gelman/research/published/mipaper.pdf

A Python program for multivariate missing-data imputation that works on large datasets!?


This last link is particularly relevant as it points to some Python code that the students can run to get started.

Of course, anyone can work on this project; no need to go to the Ideathon to do it. I guess the plan is for the Ideathon to motivate groups of people to focus.

Leave a Reply