Data and Decision-Making (+ AGM)

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Carillo Gantner Theatre, Sidney Myer Asia Centre

University of Melbourne, Swanston Street · Parkville

How to find us

AGM to take place in Staff Tea Room in Peter Hall Building, followed by the seminar in the Carillo Gantner Lecture Theatre

Location image of event venue

What we'll do

Statistical Society of Australia – Victorian AGM and seminar

5:45pm – AGM, Staff Tea Room, Peter Hall Building (, University of Melbourne
6:30pm – Seminar, Carillo Gantner Lecture Theatre, Sidney Meyer Asia Centre University of Melbourne (
7:45pm – Dinner at Café Italia in Carlton


The Annual General Meeting is open to all members of the Victorian Branch of the Statistical Society of Australia.

Meeting documents are now available:
- Minutes of last meeting (To come)
- Agenda (
- Proxy nomination forms (
- President's report (


Data and Decision-Making: Informative Missingness, Recommender Systems, and Personalised Medicine

Howard Bondell, Professor of Statistics and Data Science, University of Melbourne

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.

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