Statistics Across Campuses

                Recent Development of Rank-Constrained and Distributed Statistical Learning Date :  Thursday, 31 March 2022 Time:   10 am  - 11 am Speaker:  Assistant Professor  Ziwei Zhu Abstract: In this talk, I will present two recent works on rank-constrained least squares and distributed statistical learning respectively. The first part of the talk highlights a near optimal in-sample prediction error bound for the rank-constrained least squares estimator with no assumption on the design matrix. Lying at the heart of the proof is a covering number bound for the family of projection operators corresponding to the subspaces spanned by the design. By leveraging this complexity result, we perform a power analysis for a permutation test on the existence of a low-rank signal under the high-dimensional trace regression model. The second part of the talk proposes a new one-shot distributed learning algorithm through refitting ...

Power Analysis for Cluster Randomized Trials with Multiple Primary Endpoints Date: 1 April, Friday Time: 2pm AEDT Speaker :  Professor Song Zhang   (UT Southwestern Medical Center) Cluster randomized trials (CRTs) are widely used in different areas of medicine and public health. Recently, with the increasing complexity of medical therapies and technological advances in monitoring multiple outcomes, many clinical trials attempt to evaluate multiple primary endpoints. In this study we present a power analysis method for CRTs with K > 2 binary co-primary endpoints. It is developed based on the GEE (generalized estimating equation) approach, and three types of correlations are considered: inter-subject correlation within each endpoint, intra-subject correlation across endpoints, and inter-subject correlation across endpoints. A closed-form joint distribution of the K test statistics is derived, which facilitates the evaluation of power and type I error for arbitrarily construct...

Score matching for microbiome compositional data Date: 25 March 2022, Friday Time: 4pm AEDT Speaker: A/Prof Janice Scealy (ANU) Abstract: Compositional data are challenging to analyse due to the non-negativity and sum-to-one constraints on the sample space. It is often the case with microbiome compositional data that many of the components are highly right-skewed, with large numbers of zeros. A major limitation of currently available estimators for compositional models is that they either cannot handle many zeros in the data or are not computationally feasible in moderate to high dimensions. We derive a new set of novel score matching estimators applicable to distributions on a Riemannian manifold with boundary, of which the standard simplex is a special case. The score matching method is applied to estimate the parameters in a new flexible model for compositional data and we show that the estimators are scalable and available in closed form. We apply the new model and estimators to r...

               Factor modelling for high-dimensional functional time series Date :  Thursday, 17 March 2022 Time:   9  am  - 10 am Speaker:  Assistant Professor Xinghao Qiao Abstract: Many economic and scientific problems involve the analysis of high-dimensional functional time series, where the number of functional variables (p) diverges as the number of serially dependent observations (n) increases. In this talk, we present a novel functional factor model for high-dimensional functional time series that maintains and makes use of the functional and dynamic structure to achieve great dimension reduction and find the latent factor structure. To estimate the number of functional factors and the factor loadings, we propose a fully functional estimation procedure based on an eigenanalysis for a nonnegative definite matrix. Our proposal involves a weight matrix to improve the estimation efficiency and tackle the issue of heter...

      Bayesian Nonparametric Spectral Analysis for Gravitational Wave Astronomy. Date: Tuesday, 8 March 2022   Time: 8:00 (UTC) 19:00 (AEDT)   Speaker:  Prof Renate Meyer (University of Auckland, New Zealand) Contact the organizer: Andriy Olenko a.olenko@latrobe.edu.au Abstract:  The new era of gravitational wave astronomy truly began on September 14, 2015 with the sensational first direct observation of gravitational waves, when LIGO recorded the signature of the merger of two black holes. In the subsequent three observing runs of the LIGO/Virgo network, gravitational waves from  90 compact binary mergers have been announced. Moreover, the future space-based observatory LISA will open the low-frequency window on gravitational waves and will be sensitive to a vast range of sources including the white dwarf binaries in our Milky Way and mergers of supermassive black holes at the centre of galaxies. Beyond signal detection, a major challeng...