Brag Meeting Thursday 3rd November

When: Thursday 3rd November 2022, 13:00-14:00.

Location: GP-Y-801 and via Zoom

Included in meeting: 

The fortnightly BRAG meeting will be held this Thursday 03/11/22 at 1 pm via Zoom/GP-Y801. This week we will have presentations by @Amalan Mahendran and @Kalpani Ishara Duwalage.

Zoom link:

Password: brag@QUT (if prompted)

Amalan’s Talk

Title: A model robust subsampling approach for Generalised Linear Models in big data settings

AbstractIn today’s modern era of big data, computationally efficient and scalable methods are needed to support timely insights and informed decision making. One such method is subsampling, where a subset of the big data is analysed and used as the basis for inference rather than considering the whole data set. A key question when applying subsampling approaches is how to select an informative subset based on the questions being asked of the data. A recent approach for this has been proposed based on determining subsampling probabilities for each data point, but a limitation of this approach is that appropriate subsampling probabilities rely on an assumed model for the big data. In this article, to overcome this limitation, we propose a model robust approach where a set of models is considered, and the subsampling probabilities are evaluated based on the weighted average of probabilities that would be obtained if each model was considered singularly. Theoretical support for such an approach is provided. Our model robust subsampling approach is applied in a simulation study and in two real world applications where performance is compared to current subsampling practices. The results show that our model robust approach outperforms alternative approaches.

Ishara’s Talk

Title: Enhancing predictive capabilities in the Livestock supply chain

Abstract: The livestock supply chain, particularly, the cattle industry, significantly contributes to the Gross Domestic Product in Australia. Over recent years, the use of historical data for decision-making has become more popular in the agricultural sector. Currently, in Australia, large volumes of data are collected on cattle farms, but the value of these data is often wasted due to a lack of appropriate analysis tools and insights. Strategies to enhance the profitability and sustainability of production systems are therefore vital. In this research project, we use statistical and machine learning techniques to address some key issues in the industry related to production and re-production. In this talk, I will briefly discuss the work we have done during the past 6 months.

Kind regards,

Jamie & Katie (BRAG Co-chairs)

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