|Lecturers||Dr. Julie Josse, CMAP, Ecole Polytechnique, Paris|
|Certificate||Confirmation of participation|
Novice and advanced R users from all professional groups.
In many applied settings the data is often incomplete, which makes data analysis a challenging task. There is, however, an abundant literature, as well as more than 150 R packages that address the issue of missing data. This workshop provides a clear overview of the different methods and strategies to handle missing values. The workshop will focuss on (a) the inferential framework that aims to estimate parameters (including their variance) in the presence of missing data, (b) matrix completion methods that aim to impute as well as possible, (c) recent developments in supervised learning in the presence of missing data.
Other topics include:
During the workshop, participants will learn about as well as practice using R packages such as: mice, missMDA, Amelia, etc. All of which can be found on the Rmisstatic https://rmisstastic.netlify.com/ plateform along with a dedicated task view, which aims at giving an overview of main references, contributors, and tutorials on data analysis in the presence of missing data.
February 27-28, 2020
|After registering you will receive a short automatic confirmation by email. If you received this email you are successfully and bindingly registered for the course. For administrative reasons the written invoice won't be sent out until about two weeks before the course.|