## Archived seminars in StatisticsSeminars 1 to 50 | Next 50 seminars |

### Joint Statistics Seminar Joel Carman, Jessica Allen, Lara Najim

*University of Otago*

Date: Thursday 6 October 2022

Joel Carman

Forecasting of volcanic eruptions can be challenging due to the small amount of data available in the eruption records. The data from volcanos which share similar physical properties and statistical behaviour (statistical analogues) have been used to help estimate model parameters and forecast future eruptions at a target volcano. This project uses a series of hierarchical renewal processes and trend renewal processes, as a way to forecast the next VEI ≥ 3 eruption at Mt Taranaki by using the eruption records from Mt Taranaki and different sets of statistical analogue volcanos. Model averaging is used to combine the posterior distribution of the forecast times from each of the considered models to allow for model uncertainty.

Spatiotemporal variation in low frequency earthquake recurrence along the San Andreas fault

Jessica Allen

Major earthquakes have devastating impacts on both human and wildlife activity, and are relatively unpredictable with current seismic monitoring technology. Modelling other forms of persistent fault activity, such as low frequency earthquakes (LFEs), provides an opportunity to better understand the unobservable processes underlying large earthquakes. My Masters project uses hidden Markov models to analyse patterns of LFE activity detected at a wide range of positions along the San Andreas Fault. This will allow better understanding of the evolution and migration of activity, provide clues about changes in underlying fault composition, and enable us to link LFE activity patterns to slow slip events and thus large earthquakes.

Weathering the storm: Space weather forecasting using Hidden Markov models

Lara Najim

The study of space weather concerns interactions between the Sun and Earth. Charged particles originating from the Sun entering Earth’s atmosphere (called solar wind) interact with the Earth’s magnetic field. Strong solar wind can lead to perturbations of the magnetic field on the surface of the Earth called geomagnetic storms. Extreme geomagnetic storms can damage energy infrastructure, causing power outages and danger to human life. This project develops statistical models to categorise the activity of geomagnetic storms to understand the temporal occurrence patterns of storms with different magnitudes, with the aim to forecast large geomagnetic storms.

### Matthew Schofield

*Statistics Department University of Otago*

Date: Thursday 15 September 2022

### Sara Algeri

*University of Minnesota*

Date: Thursday 14 July 2022

### Peter Dillingham

*University of Otago*

Date: Thursday 27 May 2021

We approach this discussion through the lens of multi-driver experiments, focusing on split-plot designs. Split-plot experiments manipulate and replicate factors at different levels, usually due to logistical constraints such as the number of available fields in an agricultural experiment, or header tanks available for an ocean global change experiment (Figure 1). However, the split-plot nature of experiments is commonly ignored, leading to charges of pseudoreplication.

Rather than echoing others’ criticism of pseudoreplication, we examine when it is, and is not, an issue. Importantly, there are instances where an ‘incorrect’ analysis with pseudoreplication substantially outperforms a ‘correct’ split-plot analysis; in other instances, the incorrect analysis performs abysmally. Here, we describe a model-averaging approach we developed, explain why many laboratory-based experiments may benefit from it, and how this work informs the discussion around pseudoreplication.

This is joint work with Chuen Yen Hong, Christopher Cornwall, David Fletcher, Christina McGraw, and Jiaxu Zeng

### Darryl I. MacKenzie

*Proteus*

Date: Thursday 20 May 2021

Camera traps are widely used throughout the globe to study a broad range of species in many different ecosystems. When individuals of the species are uniquely identifiable from the images, conventional, or spatially explicit, mark-recapture methods may be used to estimate abundance, or density, and related parameters. When individuals are not uniquely identifiable, the data has often been used in occupancy-style analyses where images of the target species are regarded as a detection of the species presence at the camera location, which is sometimes an unsatisfactory use of the data. For many years it has been a commonly held viewpoint that abundance estimation for these types of species is difficult without making restrictive assumptions due to concerns about potential double counting of the same individuals, and imperfect detection due to motion sensors not triggering.

However in recent years there have been a few applications where it has been demonstrated that it is possible to obtain unbiased estimates of abundance for such species, with relatively few assumptions, provided that: 1) the field of view for each camera can be reliably determined; and 2) detection probability can be estimated (when necessary).

In this talk I shall summarise the underlying concepts behind the approaches, how they could lead to more flexible modelling of the populations, and important study design requirements that likely differ from how many camera trapping studies are undertaken at present.

### Prof Andrew Robinson

*University of Melbourne*

Date: Thursday 13 May 2021

### Professor Murray Aitkin

*University of Melbourne*

Date: Thursday 6 May 2021

This talk traces these developments back to the arguments between Fisher and Neyman over the roles of models and likelihood in statistical inference. For many flexible model-free analyses there is a model-based analysis in the background. We illustrate with examples of the bootstrap and smoothing.

### James Curran

*University of Auckland*

Date: Thursday 29 April 2021

A friend of mine once said, ''Anything new in this talk is a typo''. I add to this my corollary, ''But you might not have heard it before''.

### Hongbin Guo and Yong Wang

*University of Otago and University of Auckland*

Date: Thursday 25 March 2021

### Bruce Weir

*Department of Biostatistics, University of Washington*

Date: Friday 5 February 2021

### David Huijser

*Department of Statistics, University of Auckland*

Date: Monday 7 December 2020

### Dr Xun Xiao

*School of Fundamental Sciences, Massey University*

Date: Monday 7 December 2020

### Colin Fox

*Physics University of Otago*

Date: Tuesday 17 November 2020

### Narun Pat

*Department of Psychology University of Otago*

Date: Thursday 1 October 2020

### Taylor Hamlin

*Mathematics and Statistics Department University of Otago*

Date: Thursday 24 September 2020

### Natalie Medlicott

*University of Otago*

Date: Thursday 13 August 2020

### Mark Stirling

*Department of Geology*

Date: Thursday 30 July 2020

### Jie Kang

*Mathematics and Statistics, University of Otago*

Date: Thursday 12 March 2020

### John Hinde

*National University of Ireland, Galway*

Date: Thursday 10 October 2019

### Phillip Wilcox

*Mathematics and Statistics, University of Otago*

Date: Thursday 3 October 2019

### Varvara Vetrova

*University of Canterbury*

Date: Thursday 19 September 2019

### Peter Dillingham

*Mathematics and Statistics, University of Otago*

Date: Thursday 12 September 2019

### David Fletcher

*Mathematics and Statistics, University of Otago*

Date: Thursday 5 September 2019

### Lisa Avery

*University of Otago Mathematics and Statistics Department*

Date: Thursday 22 August 2019

### David Bryant

*Mathematics and Statistics, University of Otago*

Date: Thursday 15 August 2019

### Matthew Parry

*Department of Mathematics and Statistics*

Date: Thursday 8 August 2019

### Steven Mills

*Department of Computer Science*

Date: Tuesday 30 July 2019

### Matthew Schofield

*Department of Mathematics and Statistics*

Date: Thursday 18 July 2019

### David Eyers

*Department of Computer Science*

Date: Thursday 23 May 2019

~~David has broad research interests in computer science topics, including distributed systems and information security. One theme of his research has been seeking security techniques that are usable and accessible to end users and software developers.~~

### Assoc. Prof. Darryl MacKenzie

*Proteus & Department of Mathematics and Statistics*

Date: Thursday 16 May 2019

### Michael Lee

*University of Otago Statistics*

Date: Thursday 9 May 2019

### Timothy Bilton

*Department of Mathematics and Statistics*

Date: Thursday 2 May 2019

### Dr Jill Haszard

*Division of Sciences Biostatistician*

Date: Thursday 18 April 2019

### Amina Shahzadi

*Department of Mathematics and Statistics*

Date: Thursday 11 April 2019

### Richard Barker

*PVC, Division of Sciences*

Date: Thursday 4 April 2019

### Cheryl Quinton

*AbacusBio Limited, Dunedin*

Date: Thursday 28 March 2019

### Andrew Robinson

*University of Melbourne*

Date: Thursday 21 March 2019

### Simon Spencer

*University of Warwick*

Date: Thursday 14 March 2019

### Murray Efford

*Department of Mathematics and Statistics*

Date: Thursday 7 March 2019

### Ken Dodds

*AgResearch*

Date: Thursday 28 February 2019

### Abdis Sattar

*Department of Population and Quantitative Health Sciences Case Western Research University USA*

Date: Tuesday 19 February 2019

### Marzia Cremona

*Penn State University*

Date: Wednesday 30 January 2019

### Geoff Jones

*Massey University*

Date: Thursday 25 October 2018

### Benoit Auvray

*Iris Data Science & Department of Mathematics and Statistics*

Date: Thursday 27 September 2018

### Matt Schofield

*Department of Mathematics and Statistics*

Date: Thursday 20 September 2018

### Alastair Lamont

*Department of Mathematics and Statistics*

Date: Thursday 13 September 2018

### Ting Wang

*Department of Mathematics and Statistics*

Date: Thursday 6 September 2018

We developed a 2D hidden Markov model to automatically analyse and forecast the spatiotemporal behaviour of tremor activity in the regions Kii and Shikoku, southwest Japan. This new automated procedure classifies the tremor source regions into distinct segments in 2D space and infers a clear hierarchical structure of tremor activity, where each region consists of several subsystems and each subsystem contains several segments. The segments can be quantitatively categorized into three different types according to their occurrence patterns: episodic, weak concentration, and background. Moreover, a significant increase in the proportion of tremor occurrence was detected in a segment in southwest Shikoku before the 2003 and 2010 long-term slow slip events in the Bungo channel. This highlights the possible correlation between tectonic tremor and slow slip events.

### Zulfi Jahufer

*AgResearch and Massey University*

Date: Thursday 23 August 2018

~~Dr Zulfi Jahufer is a senior research scientist in quantitative genetics and forage plant breeding. He also conducts the Massey University course in plant breeding. His seminar will focus on the development of novel forage grass and legume cultivars; he will also introduce the new plant breeding software tool DeltaGen.~~

### Martin Hazelton

*Massey University*

Date: Thursday 16 August 2018

In principle such sampling can be conducted using Markov chain Monte Carlo methods, through a random walk on the lattice polytope. However, it is challenging to design algorithms for doing so that are both computationally efficient and have guaranteed theoretical properties. In this talk I will describe some current work that seeks to combine methods from algebraic statistics with geometric insights in order to develop and study new polytope samplers that address these issues.

### David Bryant

*Department of Mathematics and Statistics*

Date: Thursday 9 August 2018