Statistics Across Campuses

I nferring genetic linkage maps from high-throughput sequencing data Date: 05 June 2020, Friday Time: 2pm Speaker: Dr Matthew Schofield (University of Otago) Abstract: Genetic maps are usually the starting point for many types of genetic analysis. They are one-dimensional representations of genetic inheritance across a chromosome. Genetic maps frequency are commonly inferred from estimates of a hidden Markov model (HMM) since only the expression and not the transmission of genetic information is observed. No general approaches exist for assessing the uncertainty of the map. In this talk, we will obtain genetic maps and associated uncertainty for data arising from high-throughput sequencing (HTS). HTS technology provides high density data from a large numbers of individuals in a cost- and time-efficient manner. However, the observed data from HTS are more error prone than previous technologies. We first extend the HMM to account for error introduced by HTS. We then us...

Social media analysis and COVID-19 Date: 5 June 2020, Friday Time: 3 pm Speaker:  Dr  Lewis Mitchell (University of Adelaide)  Abstract: The COVID-19 pandemic has produced a number of areas where mathematical modelling and data science might make important contributions to the public health response. Concurrently, it has led to a unique improvement in the number of datasets (some anonymised, some not) being provided by typically-ungenerous tech companies to researchers to potentially assist with this response. This talk will explore how we are utilising a few of these datasets coming from the large social media platforms to attack COVID-related problems, including: Measuring social distancing and predicting risk using Facebook data Quantifying the ‘arc’ of patient experience of COVID-19 using Reddit Contact tracing: tracking public sentiment towards the COVIDSafe app using Twitter, and modelling app effectiveness Zoom link:     https://macqu...

The use of Fast Fourier Transforms and Generalized Poissonian distribution to study COVID Deaths Date: 22 May 2020, Friday Time: 3 pm Speaker: A/Prof John Nichols (Texas A&M University) Abstract: The COVID Fatality data is often grouped into subsets that represent political boundaries, if these political boundaries represent unique compact urban areas fully contained in the urban sense then the application of the SEIR model appears to be somewhat applicable, but, if this is not the case, the assumptions that are made for the SEIR model may result in a poor predictive model. The use of Fast Fourier transforms of the residual data from an exponential regression analysis provides a method to estimate the frequency response of the residuals, which can be used to review the SEIR modelling of the urban area.  The second method is a GPD analysis of the daily ratio of the fatalities to the prior day, which may prove to be a method to determine unique compactness. Examples u...

Optimizing the Fitting of Linear Mixed Models - Comparing BLAS Subroutines in Isolation (no pun intended) Date: 22 May 2020, Friday Time: 2 pm Speaker: Luke Mazur (Univeristy of Wollongong) Abstract: Linear mixed models arising from animal and plant breeding result in sparse sets of Mixed Model Equations with particular structures. An effective method of fitting these models is the Average Information (AI) algorithm, and the largest computational bottlenecks in the AI algorithm are the solution of these equations and the calculation of the Sparse Inverse Subset for the AI updating. There are a number of potential Basic Linear Algebra Sublibrary (BLAS) subroutines that can be used for these tasks, and potential candidates are investigated via a comparative experiment to see which combination of these is best. Link: https://uow-au.zoom.us/j/91318598806 Video: 

Estimation of long-memory parameter in stationary and non-stationary curve time series Date: 28 May 2020, Thursday Time: 10 am Speaker: A/Prof. Hanlin Shang (ANU) Abstract: We study a functional version of fractionally integrated stationary and nonstationary time series, covering the functional unit root as a special case. The functional time series are projected onto a finite number of sub-spaces, the level of stationarity/non-stationary allowed to vary over them. Through the classic functional principal component analysis of the sample variance operator, we obtain the eigenvalues and eigenfunctions which span a sample version of the dominant subspace. Furthermore, we introduce a simple ratio criterion to consistently estimate the dimension of the dominant sub-space, and use a memory parameter estimator, such as local Whittle estimator, to estimate the memory parameter. Monte-Carlo simulation studies and empirical applications are given to examine the finite-sample performance...

Ensembles of Trees and CLT's: Inference and Machine Learning Date: 14 May 2020, Thursday Time: 10 am Speaker: Professor Giles Hooker (ANU) Abstract: This talk develops methods of statistical inference based around ensembles of decision trees: bagging, random forests, and boosting. Recent results have shown that when the bootstrap procedure in bagging methods is replaced by sub-sampling, predictions from these methods can be analyzed using the theory of U-statistics which have a limiting normal distribution. Moreover, the limiting variance that can be estimated within the sub-sampling structure. Using this result, we can compare the predictions made by a model learned with a feature of interest, to those made by a model learned without it and ask whether the differences between these could have arisen by chance. By evaluating the model at a structured set of points we can also ask whether it differs significantly from an additive model. We demonstrate these results in an applicat...

Application of statistical Quality control in clinical area Date: 08 May, Friday Time: 3-4pm Speaker: A/Prof Mali Abdollahain (RMIT University) Abstract: While statistical quality control and profile monitoring have been extensively used in manufacturing area, their application in clinical area has just been started. In clinical monitoring, there are always more than one quality characteristics of interest which are usually correlated. In such cases, multivariate control charts should be deployed to monitor the medical process. In manufacturing industry, Profile monitoring systems assist and help to identify factors related to an observed phenomenon, assess the effect of changing any factor/s on the event and predict the behaviour of the phenomenon under different situations. In many situations the quality and performance of a medical process may be better characterized and summarized by relationship between the response (dependent) variable and one or more explanatory (indepen...

Forecasting nonlocal climate impacts for mobile marine species using extensions to empirical orthogonal function analysis Date: 08 May 2020, Friday Time: 2 pm Speaker: Dr James Thorson (NOAA) Abstract: Societal responses to COVID-19 have illustrated the great public value of accurate epidemiological forecasts; climate change has a similar potential to upend commerce and necessitates accurate decadal forecasts of community impacts. In this talk, I discuss modern extensions to Empirical Orthogonal Function (EOF) analysis and how it can be used to jointly analyze climate change and community ecology. EOF analysis is widely used to identify modes of variability from spatially distributed environmental measurements (e.g., the El Niño Southern Oscillation is primary mode of variability in sea surface temperatures in the Pacific Ocean), but is less common in community (or epidemiological) modelling to understand modes of community variability. I specifically argue that EOF analysis is one po...