Statistics Across Campuses

Depth for Curve Data and Applications Date: Friday, 1 May 2020 Time:   3-4pm Speaker:  A/Prof  Pierre Lafaye de Micheaux (UNSW) Abstract:   John W. Tukey (1975) defined statistical data depth as a function that determines the centrality of an arbitrary point with respect to a data cloud or to a probability measure. During the last decades, this seminal idea of data depth evolved into a powerful tool proving to be useful in various fields of science. Recently, extending the notion of data depth to the functional setting attracted a lot of attention among theoretical and applied statisticians. We go further and suggest a notion of data depth suitable for data represented as curves, or trajectories, which is independent of the parametrization. We show that our curve depth satisfies theoretical requirements of general depth functions that are meaningful for trajectories. We apply our methodology to diffusion tensor brain images and also to hurrica...

Representativeness and Generalisability of Inference for Samples of Networks Date: 01 May 2020, Friday Time: 4 pm Speaker: Dr Pavel Krivitsky (UNSW) Abstract: Joint modelling of large samples of networks collected from similar settings—classrooms, households, etc.—has a long history, with a variety of methods available to pool information in model estimation and inference. In the exponential-family random graph modelling framework, these methods range from post-hoc two-stage meta-analyses to sophisticated multilevel approaches. However, relatively little attention has been devoted to the generalisability of this inference, especially when the sample of networks is effectively a convenience sample, and when the population of networks is heterogeneous in size and composition. We consider two samples of within-household contact networks in Flanders, Belgium, which used very similar survey instruments but very different sampling designs: 1) a sample of 318 households, selected based on hav...

Design and Analytical Issues in Cluster Randomized Trials Date: 24 April 2020, Friday Time: 10 am Speaker: Professor Melanie Bell (University of Arizona) Abstract: Cluster randomized trials (CRTs) are studies where groups of people, rather than individuals, are randomly allocated to intervention or control. While these type of designs can be appropriate and useful for many research settings, care must be taken to correctly design and analyze them. This talk will give an overview of cluster trials, and various methodological research projects on cluster trials that I’ve been undertaken: designing CRTs, the use of GEE with small number of clusters, handling missing data in CRTs, and analysis using mixed models. I will demonstrate methods with an example from a recently completed trial on reducing cardiovascular risk among Mexican diabetics. Bio: Melanie Bell is a Professor in the Department of Epidemiology and Biostatistics at the College of Public Health, University of Arizona, and th...

Depth of Curve Data and Applications Date: 17 April 2020 Time: 4:00pm Speaker: A/Prof Pierre Lafaye de Micheaux (UNSW) Abstract: John W. Tukey (1975) defined statistical data depth as a function that determines  the centrality of an arbitrary point with respect to a data cloud or to a probability measure.  During the last decades, this seminal idea of data depth evolved into a powerful  tool proving to be useful in various fields of science. Recently, extending the notion of data depth to the functional setting attracted a lot of attention among theoretical and applied statisticians. We go further and suggest a notion of data depth suitable for data represented as curves, or trajectories, which is independent of the parametrization. We show that our curve depth satisfies theoretical requirements of general depth functions that are meaningful for trajectories. We apply our methodology to diffusion tensor brain images and also hurricane trac...

Quasi ‐ Monte Carlo sampling method  Date: 17 April 2020 Friday Time: 3-4pm  Speaker: Dr. Houying Zhu  Abstract: The Monte Carlo method is one of the widely used numerical methods for simulating probability distributions by computer ‐ generated pseudorandom numbers. Quasi ‐ Monte Carlo (QMC) methods, which can be seen as a deterministic version of Monte Carlo methods, have been developed to improve the convergence rate to achieve greater accuracy, which partially depends on generating samples with a small discrepancy. In this talk, we consider the role of quasi ‐ Monte Carlo idea in statistical sampling and propose the explicit construction of low discrepancy sequences with respect to non ‐ uniform distributions. We also would like to illustrate how to use QMC in practice, for instance, by integrating QMC rules with Markov Chain Monte Carlo framework for statistical learning problems such as variable selection.  Zoom link: macquarie.zoom.us/j/7108...