06.11.2024, 14:00 - 16:00
– Campus Golm, Building 9, Room 2.22 and via Zoom
Institutskolloquium
Graphon Models for Inhomogeneous Random Graphs
Olga Klopp (Paris), Nicolas Verzelen (Montpellier)
Torben Sell, University of Edinburgh
Missing data are ubiquitous in modern statistics, posing a major challenge in a plethora of applications. In the first half of the talk, I will firstly introduce the general missing data problem and describe different approaches to deal with it. I will focus in particular on classification problems, where a practitioner is presented with the task of assigning a new observation to one of two classes, based on a training set of labelled data. In the second half of the talk, I will motivate a new nonparametric framework for classification problems in the presence of missing data, and propose a new method, called the Hard-thresholding Anova Missing data (HAM) classifier, which not only has better theoretical properties than off-the-shelf classifiers, but also performs well in numerical experiments.