Statistics Across Campuses

Binary Response Models for Heterogeneous Panel Data with Interactive Fixed Effects Date :  Wednesday, 30 November 2022  Time:   3:30  - 4:30 pm Speaker:  Associate Professor Bin Peng (Monash University) Abstract: In this talk, we investigate binary response models for heterogeneous panel data with interactive fixed effects by allowing both the cross-sectional dimension and the temporal dimension to diverge. From a practical point of view, the proposed framework can be applied to predict the probability of corporate failure, conduct credit rating analysis, etc. Theoretically and methodologically, we build a link between a maximum likelihood estimation and a least squares approach, provide a simple information criterion to detect the number of factors, and establish the corresponding asymptotic theory. In addition, we conduct intensive simulations to examine the theoretical findings. In an empirical study, we focus on the sign prediction of stock returns, and then use the results of sign

Estimation, diagnostics, and extensions of nonparametric Hawkes processes Date: 2 December 2022 Time: 4pm AEDT Speaker:  Prof Jiancang Zhuang  ( Institute of Statistical Mathematics, Tokyo ) *****The link to the recording of the talk will be posted here***** 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 article discusses a general nonparametric framework for the estimation, extensions, and post-estimation diagnostics of Hawkes models, which can be divided into 4 steps: 1. Model design. Design the model according to the features of the observation data, specifically the particular mathematical form of the Hawkes model (parametric, nonparametric, or semiparametric), which depend on the available empirical knowledge of the studied process. 2. Estimation

Safeguarding National Digital Memory: Bayesian Network modelling of digital preservation risks Date: 15 November 2022, Tuesday Time: 2pm AEDT Speaker: Dr Martine J Barons (University of Warwick) *****The recording of Dr Barons' talk can be found here:    https://youtu.be/Dhjyjz7Nr1c ***** Abstract: The National Archives, UK, identified a need to be able to assess risks to archives held digitally in order to ensure their longevity. Working with the Applied Statistics & Risk Unit at the University of Warwick, UK, a decision-support tool was developed. Archives comprise primary sources, which may be physical, born-digital or digitised. Digital records have a limited lifespan through carrier degradation, software and hardware obsolescence and storage frailties. It is important that the original bitstream of these primary sources is preserved and can be demonstrated to have been preserved. Soft elicitation with experienced archivists was used to identify the most likely elements con

    Exploring the Limits of the Bayesian Universe: How to Tackle Breadth and Depth. Date: Tuesday, 8 November 2022 Time: 8:00 (UTC) Speaker:  A/Prof. Aaron Robotham (University of Western Australia)   Contact the organizer: Andriy Olenko a.olenko@latrobe.edu.au Abstract: In the last 10 years it is notable that students are much more enthused about projects involving “machine learning”, but it is important we do not lose perspective on the scientific insights still offered by a comprehensive and pragmatic application of Bayesian principles. Here I will discuss the work my group has undertaken over the last 7 years to build up a fully generative model of galaxies that has culminated in the Bayesian modelling software ProFuse (Robotham+ 2022). The positive is that encoding our knowledge and ignorance in a Bayesian manner has opened up new insights to physical processes that form galaxies, the negative is that this approach has a high barrier of entry which can be a poor fit to a modern