Abstract: In many applications, it is of interest to cluster
subjects based on very high-dimensional data, often in the presence
of missing data. Although discrete mixture models are routinely
used, we demonstrate pitfalls in high-dimensional...
Abstract: In today’s data-driven society, human beings still
make most critical decisions and yet they are increasingly
utilizing recommendations produced by statistical and machine
learning methods. Given the prevalence of this approach in
many...
Abstract: An abundant literature addresses missing data in an
inferential framework: estimating parameters and their variance
from incomplete tables. Here, we consider supervised-learning
settings: predicting a target when missing values appear in...
Abstract: There is a very extensive literature of statistical
methods for the analysis of data with missing values. I'll provide
a historical overview, including ad-hoc approaches, statistical
models and conditions for ignoring the missingness...