Statistics Across Campuses

Weak differentiability applied to the profile likelihood estimation in a joint model of longitudinal and survival data. Date :  Thursday, 3 June 2021 Time:   10 -11 am Speaker:  Dr Yuichi Hirose  ( Victoria University of Wellington) . Abstract: There is a difficulty in finding an estimate of variance of the profile likelihood estimator in the joint model of longitudinal and survival data.  We solve the difficulty by applying the weak differentiation to the implicit function in the profile likelihood estimation.  The derivative is used to show the asymptotic normality of the profile likelihood estimator without assuming the second derivative of the profile likelihood exists. Zoom Link:  Please contact Yanrong Yang ( yanrong.yang@anu.edu.au ) to obtain the zoom link for this seminar.

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 as Diplom–Mathematiker from the Rheini

Max-Infinitely Divisible Models and Inference for Spatial Extremes, with Application to Non-Stationary Heatwave Hazard Assessment Date: 07 June 2021, Monday Time: 4pm AEDT Speaker: Dr Raphaël Huser (King Abdullah University of Science and Technology) Abstract: The modeling of spatio-temporal trends in temperature extremes can help better understand the structure and frequency of heatwaves in a changing climate. Here, we study annual temperature maxima over Southern Europe using a century-spanning dataset observed at 44 monitoring stations. Extending the spectral representation of max-stable processes, our modeling framework relies on a novel construction of max-infinitely divisible processes, which include covariates to capture spatio-temporal non-stationarities. Our new model keeps a popular max-stable process on the boundary of the parameter space, while flexibly capturing weakening extremal dependence at increasing quantile levels and asymptotic independence. This is achieved by li

    Most Powerful Test against High Dimensional Local Alternatives Date :  Thursday, 27 May 2021 Time:   3 - 4 pm AEDT Canberra Speaker:  Dr Yi He (University of Amsterdam) Abstract: We develop a powerful quadratic test for the overall significance of many covariates in a dense regression model in the presence of nuisance parameters. By equally weighting the sample moments, the test is asymptotically correct in high dimensions even when the number of coefficients is larger than the sample size. Our theory allows a non-parametric error distribution and weakly exogenous nuisance variables, in particular autoregressors in many applications. Using random matrix theory, we show that the test has the optimal asymptotic testing power among a large class of competitors against local alternatives whose coordinates are dense in the eigenbasis of the high dimensional sample covariance matrix among regressors.  The asymptotic results are adaptive to the covariates’ cross-sectional and temporal dep

Monte Carlo variance reduction using Stein operators  Date :  Friday, 21 May 2021 Time:  2 -3 pm Speaker:   Dr. Leah South ( Queensland University of Technology ) Abstract: This talk will focus on two new methods for estimating posterior expectations when the derivatives of the log posterior are available. The proposed methods are in a class of estimators that use Stein operators to generate control variates or control functionals. The first method applies regularisation to improve the performance of popular Stein-based control variates for high-dimensional Monte Carlo integration. The second method, referred to as semi-exact control functionals (SECF), is based on control functionals and Sard’s approach to numerical integration. The use of Sard’s approach ensures that our control functionals are exact on all polynomials up to a fixed degree in the Bernstein-von-Mises limit. Several Bayesian inference examples will be used to illustrate the potential for reduction in mean square error.

     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 positive, and therefore impose additional restrictions on the coefficients. In t

    Central Limit Theorem for Linear Spectral Statistics of Large Dimensional Kendall's Rank Correlation Matrices and its Applications Date :  Thursday, 6 May 2021 Time:   10 -11 am Speaker:  Associate Professor Zeng Li  ( Southern University of Science and Technology) . Abstract: In this talk we will talk about the limiting spectral behaviors of large dimensional Kendall’s rank correlation matrices generated by samples with independent and continuous components. The statistical setting covers a wide range of highly skewed and heavy-tailed distributions since we do not require the components to be identically distributed, and do not need any moment conditions. We establish the central limit theorem (CLT) for the linear spectral statistics (LSS) of the Kendall’s rank correlation matrices under the Marchenko-Pastur asymptotic regime, in which the dimension diverges to infinity proportionally with the sample size. We further propose three nonparametric procedures for high dimensional