SSA Canberra invites you to the Dennis Trewzin prize for 2023, showcasing outstanding early career statisticians and their research conducted in the ACT/regional NSW.
The Dennis Trewin Prize, named after the former Australian Statistician Dennis Trewin AO, is awarded annually by the Canberra Branch for outstanding early career research in an area related to statistics and/or data science conducted within the ACT or regional NSW outside Newcastle-Sydney-Wollongong. This year we have a short-list of two candidates. They will be presenting their work at our late October meeting and a selection panel will determine the winner. Each candidate will have 20-25 minutes for their presentation and 5 minutes for audience questions. In partnership with the Australian Bureau of Statistics, monetary prizes totalling $1,000 or more will be on offer and distributed at the discretion of the selection panel.
We will be having an SSA Canberra dinner that evening at a nearby venue, so if you are interested in attending the dinner and catching up with members, candidates, and friends in person, please see below for details for RSVP-ing.
Time: Start at 5:30pm and finish by 7:00pm Canberra time.
Venue: Room 4.04 in the Marie Reay Teaching Centre, the Australian National University, or via Zoom.
Zoom link: https://anu.zoom.us/j/81782141817?pwd=N3FrcVBjamg0eEVpT0FCUUhvVXFkUT09
The zoom link will be open just before 5.30pm. RSVP is not required. Full zoom details given at the end of the email.
Dinner:
After the talks we will be holding a dinner at Badger & Co in the adjoining building at ANU (Badger & Co – Uni Pub – ANU – Canberra (badgerandco.com.au) at 7.15pm. If you are interested in attending the dinner, please let me know by 4pm Monday 30 October by entering your details at SSA Canberra Branch dinner attendance sheet, or contacting me (warren.muller@csiro.au; 0407 916 868). Please regard this as a firm commitment, not just an intention. For withdrawals after the deadline, please remove your name from the sheet and phone or text me (0407 916 868).
NOTE: We are offering discounts to SSA early career and student members who attend dinner! For this meeting, dinners will be a fixed charge of $5 for student members and $10 for early career members. As the venue is card payment only, subsidised participants should pay cash to Warren Muller, who will pay for their meal by card. Other participants should purchase their own meals.
Talk details
Speaker #1: Rui Shen: On the Estimation and Selection Consistency of Network Autoregressive Model: A Non-asymptotic Viewpoint
Abstract
The network autoregressive model characterises the interaction relationships between different dimensions of the response vector via a sparse interaction matrix of parameters. Based on this model, we study two types of estimators: regularised maximum likelihood (ML) and neighbourhood-based estimators. Subsequently, we demonstrate the non-asymptotic theoretical properties of these estimators, which is the main contribution of this project. The theoretical properties of both regularised maximum likelihood and neighbourhood-based estimators are confirmed by substantial simulation experiments and a real data analysis, where the estimates are obtained based on the coordinate descent (CD) algorithm.
Biography I am a first-year statistics PhD student at ANU. My research interests include high-dimensional statistics, network influence analysis and quantitative trading algorithms.
Speaker #2: Xu Ning: A Double Fixed Rank Kriging Approach to Spatial Regression Models with Covariate Measurement Error.
Abstract In many applications of spatial regression modeling, the spatially-indexed covariates are measured with error, and it is known that ignoring this measurement error can lead to attenuation of the estimated regression coefficients. Classical measurement error techniques may not be appropriate in the spatial setting, due to the lack of validation data and the presence of (residual) spatial correlation among the responses. We propose a double fixed rank kriging (FRK) approach to obtain bias-corrected estimates of and inference on coefficients in spatial regression models, where the covariates are spatially indexed and subject to measurement error: assuming they vary smoothly in space, the proposed method first fits an FRK model regressing the covariates against spatial basis functions to obtain predictions of the error-free covariates. These are then passed into a second FRK model, where the response is regressed against the predicted covariates plus another set of spatial basis functions to account for spatial correlation.
Biography I am a 4th year PhD student at the ANU. For my PhD I have worked on: alternate likelihood methods for estimation and inference; longitudinal, spatio-temporal, and correlated data analysis; mixed effects models and semiparametric regression.
Website links: https://statsoc.org.au/Canberra-Branch-meetings
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