Statistics Across Campuses

Bespoke Realized Volatility: Tailored Measures of Risk for Volatility Prediction Speaker:  Prof. Andrew Patton   (Duke University) Date/Time:   Wednesday 10 May, 2 pm -3 pm Location:   110 Finance Decision Lab, 4 Eastern Rd / Online  via Zoom Abstract:  Standard realized volatility (RV) measures estimate the latent volatility of an asset price using high frequency  data  with  no  reference  to  how  or  where  the  estimate  will  subsequently be used. This paper presents methods for “tailoring” the estimate of volatilityto the application in which it will be used. For example,  if the volatility measure willbe  used  in  a  specific  parametric  forecasting  model,  it may  be possible  to  exploit  that information and construct a better measure of volatility.  We use methods from machine learning to estimate optimal “bespoke”...

Myth busting and apophenia in data visualisation: Is what you see really there? Date :  Monday, 29 August 2022 Time:   12:00 noon  - 1:00 pm Speaker: Prof. Dianne Cook (Monash University)  Abstract: In data science, plots of data become important tools for observing patterns, discovering relationship, busting myths, making decisions, and communicating findings. But plots of data can be viewed differently by different observers, and it is easy to imagine patterns that may not exist. This talk will describe some simple tools for helping to decide if patterns are really there, in the larger context of the problem. We will talk about two protocols, the Rorschach, which can help insulate the mind from spurious structure, and the lineup, which places the data plot in the context of nothing happening. There will be an opportunity for the audience to try out these protocols in examining data from current media. Speaker biography.  Dianne Cook is Professor of Business An...

  The Moyal Medal Committee invites you to attend the virtual lecture and presentation of the 2021 Moyal Medal to  Professor Louise Ryan  from the   University of Technology Sydney   The Moyal Medal is awarded annually for research contributions to mathematics, physics or statistics, the areas of research of the late Professor José Enrique Moyal. Professor Moyal was Professor of Mathematics at Macquarie University for five years from 1973 to 1977. His insight into the interaction between mathematics, physics and statistics led him to make contributions to these disciplines which have had far-reaching ramifications in all three fields.  2021 Medallist Professor Louise Ryan  After completing her honours degree in statistics at Macquarie University in 1978, Louise Ryan left Australia to undertake PhD studies at Harvard. On completing her PhD in 1983, she moved to the Harvard Biostatistics Department, first as a postdoctoral fellow, then a junior...

       Gaussian approximation for high-dimensional data: Recent progress Date :  Thursday, 21 October 2021 Time:   4-5 pm Speaker:  Prof Yuta Koike (University of Tokyo) Abstract: We review recent progress in Gaussian approximation of a sum of high-dimensional independent random vectors (and related statistics) on hyper-rectangles, where the dimension can be much larger than the sample size. Such approximation is useful for justifying bootstrap approximation of maximum statistics in high-dimensional data and is therefore important for uniform inference in high-dimensional models. Zoom Link:  https://macquarie.zoom.us/j/88030361110?pwd=OTAzRStXNEtmTGRyaWRKcVJLL09Pdz09

      Screening methods for linear errors-in-variables models in high dimensions Date :  Friday, 27 August 2021 Time:   3 -4 pm Speaker:  Dr   Linh Nghiem (ANU) Abstract: Microarray studies, in order to identify genes associated with an outcome of interest, usually produce noisy measurements for a large number of gene expression features from a small number of subjects. A common approach to analyzing such high-dimensional data is to use linear errors-in-variables models; however, current methods for fitting such models are computationally expensive due to non-convex optimization or sampling from high dimensional distributions. We develop two efficient screening procedures to reduce the number of variables for final model building; both of them have strong theoretical support and can be computed efficiently even with a large number of features. Through simulation studies and an analysis of microarray data, we demonstrate that the two new screening proce...

     Wind energy forecasting in an operational context: traversing research and industry Date :  Friday, 20 August 2021 Time:   3 -4 pm Speaker:  Dr  Rachael Quill (Weatherzone) Abstract: Forecasting in an operational context requires the combination of scientific rigor with computational efficiency and ongoing validation with on-the-fly development. Predicting wind energy generation in real-time necessitates modular and dynamic forecasting systems which are capable of ingesting various and variable data streams to produce consistent, reliable and accurate power predictions. From data cleaning to atmospheric processes, in this seminar we discuss case studies, from different perspectives across academia and industry, which highlight some of the challenges faced in formulating such a comprehensive operational forecast system. Short Bio:  Dr Rachael Quill is an early career statistician with experience in modelling, analysing and predicting wind fiel...

    Representativeness and Generalisability of Inference for Statistical Models for Samples of Networks Date :  Friday, 13 August 2021 Time:   3 -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 differ...

   Far and Close: Selective Sample Enrichment to Deal with Multicollinearity in Geographically Weighted Regression Date :  Friday, 30 July 2021 Time:   12-1pm Speaker:  Dr P atricia Menendez  ( Department of Econometrics and Business Statistics at Monash University ) Abstract: Geographically weighted regression (GWR) is a popular technique to deal with spatially varying relationships between a response variable and a set of predictors. However, GWR estimates might be affected by multicollinearity issues related to locally poor designs. In this study, we propose two regularization methods to deal with those issues. The first one is based on a generalized ridge regression, which can also be seen as an empirical Bayes method. We show that it can be implemented using ordinary GWR software with an appropriate choice of the weights. The second one augments the local sample using an enrichment strategy. The methods will be illustrated with simulations and with an ...

On bivariate extreme value copulas with polynomial dependence functions. Date:  Friday, 4 June 2021 Time:   3-4pm Speaker :  Associate Professor Berwin Turlach (University of Western Australia) Abstract:  We discuss how the mixed model and the asymmetric mixed model family of bivariate extreme value can be extended to bivariate extreme value copulas with polynomial dependence function of arbitrary degree. An algorithm for fitting extreme value copulas with polynomial dependence functions to data will be presented and various practical issues that arise when fitting bivariate extreme value copula models will be discussed. Zoom Link:   https://macquarie.zoom.us/j/86987542903?pwd=SUVsVXpsbVVPckFaL0wwUTlJeFJ6dz09 Bio:  Berwin Turlach is an Associate Professor at the University of Western Australia, having previously worked at the Australian National University, the University of Adelaide, and the National University of Singapore.  He received a degree...

     Functional limit theorems for financial markets with long-range dependence Date :  Friday, 21 May 2021 Time:   4-5 pm Speaker:   Professor  Yuliya Mishura (Kyiv National University, Ukraine) Abstract: We start with an additive stochastic sequence that is based on the sequence of independent identically distributed (iid) random variables and has the coefficients that allow for dependency on the past. Then we formulate the conditions of the weak convergence to some limit process in terms of coefficients and characteristic function of  any  basic  random variable. We adapt the general conditions to the case where the limit process is Gaussian. Then we move onto the multiplicative scheme in order to get a positive limit process (with the probability 1) that can be used for modelling of some asset prices from financial markets. Hence, we assume that all multipliers in the prelimit multiplicative scheme are p...

  Estimation, diagnostics, and extensions of nonparametric Hawkes processes.   Date:  Friday, 26 March 2021 Time:   3-4pm Speaker:  Assoc. Professor Jiancang Zhuang   (The Institute of Statistical Mathematics Japan and Department of Statistical Sciences, the Graduate University for Advanced Studies) . Abstract:   The Hawkes self-exciting model has become one of the most popular point-process models in many research areas in the natural and social sciences because of its capacity for investigating the clustering effect and positive interactions among individual events/particles. This talk discusses a general nonparametric framework for the estimation, extensions, and post-estimation diagnostics of Hawkes models. For illustration, I use the kernel function as the basic smoothing tool and the earthquake data and crime data as two application examples, to show how a Hawkes model is formulated from scratch. Zoom Link:  https://macquarie.zoom.us/j/84842...

  Order Selection with Confidence for Mixture Models Date:  Friday 19 March 2021 Time:  3pm Speaker:  Dr Hien Nguyen ( La Trobe University) Abstract: Finite mixture models are distribution models that are defined by convex combinations of a finite number of elements (components) from some base distribution class, where the number of elements dictates the complexity of the mixture model. Given that data arise from a class of finite mixture models, where the number of components is unknown, an important problem that arises is choice of the number of components that one should use to model the data. We present a hypothesis test-based algorithm to selecting the number of components of a mixture model that yields a lower bound on the number of components, with confidence. We demonstrate that in special circumstances, the approach also yields a method that consistently selects the correct number of components, and we demonstrate the effectiveness of the approach via a stud...

Random FPUT Lattices   Date: Thursday 11 March 2021 Time: 10am Speaker:   Professor J. Douglas Wright (Drexel University) Abstract: We consider a linear Fermi-Pasta-Ulam-Tsingou lattice with random spatially varying material coefficients. Using the methods of stochastic homogenization we show that solutions with long wave initial data converge in an appropriate sense to solutions of a wave equation. The convergence is both strong and almost sure, but the rate of convergence is quite slow. The technique combines energy estimates with powerful classical results about random walks, specifically the law of the iterated logarithm. This work is joint with Drexel PhD student, Josh McGinnis. Zoom Link:  https://macquarie.zoom.us/j/83753405912?pwd=VU96LzJqbnhVODQwMnhhTS9VNEc5Zz09

  Model-free Prediction and Regression: A Transformation-based Approach to Inference Date: Thursday  22  October 2020 Time: 11 am Speaker:  Professor  Dimitris  Politis  ( University of California at San Diego ) Abstract: Prediction has been traditionally approached via a model-based paradigm, i.e., (a) fit a model to the data at hand, and (b) use the fitted model in order to extrapolate/predict future data. Due to both mathematical and computational constraints, 20th century statistical practice focused mostly on parametric models. Fortunately, with the advent of widely accessible powerful computing in the late 1970s, computer-intensive methods such as the bootstrap and cross-validation have freed practitioners from the limitations of parametric models, and paved the way towards the ‘big data’ era of the 21st century. Nonetheless, there is a further step one may take, namely going beyond even nonparametric models. The Model-Free Prediction Principle i...

  Leveraging Pleiotropy effect from genome-wide association studies using Sparse Group Models Date:  Friday 2  October 2020 Time: 3 pm Speaker:  Prof Benoit Liquet-Weiland (Macquarie University) Abstract: Genome-wide association studies (GWAS) focus on testing association between millions of genetic markers (or single nucleotide polymorphisms, SNPs) and a phenotype in an agnostic way, where every SNP is tested independently from the other SNPs for association with the phenotype. One major finding from GWAS era is that pleiotropy – that occurs when one gene influence two or more unrelated traits - is a widespread phenomenon in human complex traits. Several methods were proposed to combine results across studies of different phenotypes in order to improve the power of detecting pleiotropic associations at SNP level. It is well established that incorporating prior biological knowledge as gene or biological pathways structures to consider complex mechanisms can help to d...

  Estimating a Covariance Function from Fragments of Functional Data Date:  Friday 11  September 2020 Time: 3 pm Speaker:  Professor Aurore Delaigle (University of Melbourne) Abstract: Functional data are often observed only partially, in the form of fragments. In that case, the standard approaches for estimating the covariance function do not work because entire parts of the domain are completely unobserved. In previous work, Delaigle and Hall (2013, 2016) have suggested ways of estimating the covariance function, based for example on Markov assumptions. In this work, we take a completely different approach which does not rely on such assumptions. We show that using a tensor product approach, it is possible to reconstruct the covariance function using observations located only on the diagonal of its domain. Zoom Link:   https://macquarie.zoom.us/j/91597976300?pwd=WVpyVEdtUXhKSEJjbHV2TVVWTXExdz09

  The geometry of forecast reconciliation Date:  Friday 28  August 2020 Time: 3 pm Speaker:  Prof Rob J Hyndman (Monash University) Abstract: It is common to forecast at different levels of aggregation. For example, a retail company will want national forecasts, state forecasts, and store-level forecasts. And they will want them for all products, for groups of products, and for individual products. Forecast reconciliation methods allow for the forecasts at all levels of aggregation to be adjusted so they are consistent with each other. I will describe a geometric interpretation for reconciliation methods used to forecast time series that adhere to known linear constraints. In particular, a general framework is established nesting many existing popular reconciliation methods within the class of projections. This interpretation facilitates the derivation of novel results that explain why and how reconciliation via projection is guaranteed to improve forecast accur...

STREAMLINED VARIATIONAL INFERENCE FOR RANDOM EFFECTS MODELS   Date:  Friday  7 August 2020  Time: 3 pm Speaker:  Prof  Matt Wand (University of Technology Sydney) Abstract: Variational inference offers fast approximate inference for graphical models arising in computer science and statistics. However, for models containing random effects, direct application of variational inference principles is not sufficient for fast inference due to the sizes of the relevant design matrices. We explain how the notion of matrix algebraic streamlining is crucial for making variational inference practical for models containing very high numbers of random effects. Both nested higher level and crossed random effect structures are discussed. Zoom Link:  https://macquarie.zoom.us/j/99218303144