Friday 2 June, 4 PM
Social to follow, 5 - 6 PM
Scott Hottovy, Associate Professor of Mathematics, United States Naval Academy
Hybrid: Red Centre 4082
https://unsw.zoom.us/j/88495626621
“Convergence of Rain Process Models”
Abstract: Representing rain in global climate models continues to be a challenge. Currently, models generally rain too often and too little. Additionally, the models have trouble capturing variability in rain data. One possible solution to increasing variability in a model is to use a stochastic process. A variety of stochastic models have been used to describe time series of precipitation or rainfall. Since many of these stochastic models are simplistic, it is desirable to develop connections between the stochastic models and the underlying physics of rain. In this talk, I will describe simple models of rain in a single column model as a stochastic differential equation (SDE) with a switch. The inclusion of this switch leads to a model with hysteresis. I will show how these models are connected by presenting formal derivations and theorems on convergence of SDEs and their Kolmogorov Equations.
Friday 9 June, 4 PM
Wei Huang, Lecturer, School of Mathematics and Statistics, University of Melbourne
Virtual
https://unsw.zoom.us/j/88495626621
“Nonparametric Estimation of General Treatment Effects using Stabilized Weights”
Abstract: Estimating the causal effect of a treatment or policy from observational studies is a challenging task due to confounding issues. We propose a nonparametric approach to identify and estimate general treatment effects using a weighted conditional expectation under unconfoundedness assumption. The weights are estimated through a generalised empirical likelihood method subject to an expanding set of moment equations. Our proposed estimator achieves semiparametric efficiency bounds for discrete treatments and is more efficient than the estimator constructed from the true weights for continuous treatments. We also derive an asymptotic influence function to facilitate statistical inference. Our framework includes a variety of treatment effects, such as average, quantile, and asymmetric least squares treatment effects. Additionally, it can be applied to data with measurement errors and functional components.
Friday 16 June, 4 PM
Kin Wai (Keith) Chan, Assistant Professor, Department of Statistics, The Chinese University of Hong Kong
Virtual
https://unsw.zoom.us/j/88495626621
“No-lose Converging Kernel Estimation of Long-run Variance”
Abstract: Kernel estimators have been popular for decades in long-run variance estimation. To minimize the loss of efficiency measured by the mean-squared error in important aspects of kernel estimation, we propose a novel class of converging kernel estimators that have the “no-lose” properties including: (1) no efficiency loss from estimating the bandwidth as the optimal choice is universal; (2) no efficiency loss from ensuring positive-definiteness using a principle-driven aggregation technique; and (3) no efficiency loss asymptotically from potentially misspecified prewhitening models and transformations of the time series. A shrinkage prewhitening transformation is proposed for more robust finite-sample performance. The estimator has a positive bias that diminishes with the sample size so that it is more conservative compared with the typically negatively biased classical estimators. The proposal improves upon all standard kernel functions and can be well generalized to the multivariate case. We discuss its performance through simulation results and two real-data applications including the forecast breakdown test and MCMC convergence diagnostics.
Friday 23 June, 10 AM
Mark Glickman, Senior Lecturer, Department of Statistics, Harvard University
Virtual
https://unsw.zoom.us/j/88495626621
“Rating competitors in games with strength-dependent tie probabilities”
Abstract: Competitor rating systems for head-to-head games are typically used to measure playing strength from game outcomes. Ratings computed from these systems are often used to select top competitors for elite events, for pairing players of similar strength in online gaming, and for players to track their own strength over time. Most implemented rating systems assume only win/loss outcomes, and treat occurrences of ties as the equivalent to half a win and half a loss. However, in games such as chess, the probability of a tie (draw) is demonstrably higher for stronger players than for weaker players, so that rating systems ignoring this aspect of game results may produce strength estimates that are unreliable. We develop a new rating system for head-to-head games that explicitly acknowledges a tie as a third outcome, and that the probability of a tie may depend on the strengths of the competitors. Our approach relies on time-varying game outcomes following a Bayesian dynamic modeling framework, and that posterior updates within a time period are approximated by one iteration of Newton-Raphson evaluated at the prior mean. The approach is demonstrated on a large dataset of chess games played in International Correspondence Chess Federation tournaments.
Friday 30 June, 4 PM
Social to follow, 5 - 6 PM
Zhi Yang Tho, PhD Candidate, Australian National University
Virtual
https://unsw.zoom.us/j/88495626621
“Joint Mean and Correlation Regression Modelling for Multivariate Data”
Abstract: In the analysis of multivariate or multi-response data, researchers are often not only interested in studying how the mean (say) of each response evolves as a function of covariates, but also and simultaneously how the correlations between responses are related to one or more similarity/distance measures. To address such research questions, we propose a novel joint mean and correlation regression model that simultaneously regresses the mean of each response against a set of covariates and the correlations between responses against a set of similarity measures, which can be applied to a wide variety of correlated discrete and (semi-)continuous responses. Under a general setting where the number of responses can tend to infinity with the number of clusters, we demonstrate that our proposed joint estimators of the regression coefficients and correlation parameters are consistent and asymptotically normally distributed with differing rates of convergence. We apply the proposed model to a dataset of overdispersed counts of 38 Carabidae ground beetle species sampled throughout Scotland, with results showing in particular that beetle total length and breeding season have statistically important effects in driving the correlations between beetle species.