COLLOQUIUM: Data and Decision‐Making: Informative Missingness, Recommender Systems, and Personalised Medicine
ABSTRACT: In this talk, we will discuss two topics associated with the use of data for decision‐making.
The first part of the talk investigates informative missingness in the framework of recommender systems. In this setting, we envision a potential rating for every object‐user pair. The goal of a recommender system is to predict the unobserved ratings and then recommend an object that the user is likely to rate highly. A typically overlooked piece is that the combinations are not missing at random. For example, in movie ratings, a relationship between the user ratings and their viewing history is expected, as human nature dictates the user would seek out movies that they anticipate enjoying. We model this informative missingness, and place the recommender system in a shared‐variable regression framework which can aid in prediction quality.
The second part of the talk deals with personalised medicine, which relies on the ability to prescribe patient‐specific treatments. In this context, it is crucial to identify the variables that impact the optimal treatment decision. Typical variable selection techniques target on selecting variables that are important for prediction, which are not necessarily those that are important for treatment assignment. We propose a Gaussian process model in a backward elimination framework to identify the important variables in treatment decision making.
Professor Howard Bondell, Professor of Statistics and Data Science
Professor Howard Bondell
Professor of Statistics and Data Science
University of Melbourne
Howard Bondell is Professor of Statistics and Data Science at the University of Melbourne, Australia. He is also Adjunct Professor at North Carolina State University, USA. Professor Bondell received his PhD in Statistics from Rutgers University in 2005. He began his academic career in 2005 in the Department of Statistics at North Carolina State University, and moved to the School of Mathematics and Statistics at the University of Melbourne in 2018. His research interests include variable and model selection, robust estimation, quantile regression, nonparametric smoothing and regression, regularization and Bayesian methods. Professor Bondell was elected Fellow of the American Statistical Association in 2017.