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

### Alastair Lamont

*Department of Mathematics and Statistics*

Date: Thursday 20 July 2017

### Michael Lee

*Department of Mathematics and Statistics*

Date: Thursday 13 July 2017

### Honours and PGDip students

*Department of Mathematics and Statistics*

Date: Friday 2 June 2017

Jodie Buckby :

*Model checking for hidden Markov models*

Jie Kang :

*Model averaging for renewal process*

Yu Yang :

*Robustness of temperature reconstruction for the past 500 years*

MATHEMATICS

Fergus O'Leary :

*Stochastic spatial population models*

Rachael Young :

*SIR epidemic models on networks*

Sam Bremer :

*An effective model for particle distribution in groundwaters*

Joshua Mills :

*Hyperbolic equations and finite difference schemes*

Yiwen Qi :

*The heat equation and Brownian motion*

Vijay Surada :

*Modelling the self-thinning rule*

### Jim Cotter

*School of Physical Education, Sport and Exercise Sciences*

Date: Thursday 1 June 2017

### Amina Shahzadi

*Department of Mathematics and Statistics*

Date: Thursday 25 May 2017

### Jin Zhang

*Department of Accountancy and Finance*

Date: Thursday 18 May 2017

### Paula Bran

*Department of Mathematics and Statistics*

Date: Thursday 11 May 2017

### Richard Arnold

*Victoria University Wellington*

Date: Thursday 4 May 2017

This is joint work with Stefanka Chukova (VUW) and Yu Hayakawa (Waseda University, Tokyo)

### Fiona Hely

*AbacusBio*

Date: Thursday 27 April 2017

### Will Rayment

*Department of Marine Science*

Date: Thursday 13 April 2017

### Peter Dillingham

*Department of Mathematics and Statistics*

Date: Thursday 6 April 2017

### Mike Paulin

*Department of Zoology*

Date: Thursday 30 March 2017

*not*doing this in late Precambrian ecosystems leads us to model spike trains recorded from sensory neurons (in sharks, frogs and other animals) as samples from a family of Inverse Gaussian-censored Poisson, a.k.a. Exwald, point-processes. Neurons that evolved for other reasons turn out to be natural mechanisms for generating samples from Exwald processes, and natural computers for inferring the posterior density of their parameters. This is a consequence of a curious correspondence between the likelihood function for sequential inference from a censored Poisson process and the impulse response function of a neuronal membrane. We conclude that modern animals, including humans, are natural Bayesians because when neurons evolved 560 million years ago they provided our ancestors with a choice between being Bayesian or being dead.

This is joint work with recent Otago PhD students Kiri Pullar and Travis Monk, honours student Ethan Smith, and UCLA neuroscientist Larry Hoffman.

### Nicolas Cullen

*Department of Geography*

Date: Thursday 23 March 2017

### Farzana Afroz

*Department of Mathematics and Statistics*

Date: Thursday 16 March 2017

### Tilman Davies

*Department of Mathematics and Statistics*

Date: Thursday 9 March 2017

*d*-dimensional spatial data can be represented as a slice of a

*fixed*-bandwidth kernel estimator in

*(d+1)*-dimensional "scale space", enabling fast computation using discrete Fourier transforms. Edge correction factors have a similar representation. Different values of global bandwidth correspond to different slices of the scale space, so that bandwidth selection is greatly accelerated. Potential applications include estimation of multivariate probability density and spatial or spatiotemporal point process intensity, relative risk, and regression functions. The new methods perform well in simulations and real applications.

Joint work with Professor Adrian Baddeley, Curtin University, Perth.

### Jiancang Zhuang

*Institute of Statistical Mathematics, Tokyo*

Date: Thursday 2 March 2017

### Shiyong Zhou

*Peking University*

Date: Tuesday 14 February 2017

**NOTE venue is not our usual**

Following the stress release model (SRM) proposed by Vere-Jones (1978), we developed a new multidimensional SRM, which is a space-time-magnitude version based on multidimensional point processes. First, we interpreted the exponential hazard functional of the SRM as the mathematical expression of static fatigue failure caused by stress corrosion. Then, we reconstructed the SRM in multidimensions through incorporating four independent submodels: the magnitude distribution function, the space weighting function, the loading rate function and the coseismic stress transfer model. Finally, we applied the new model to analyze the historical earthquake catalogues in North China. An expanded catalogue, which contains the information of origin time, epicentre, magnitude, strike, dip angle, rupture length, rupture width and average dislocation, is composed for the new model. The estimated model can simulate the variations of seismicity with space, time and magnitude. Compared with the previous SRMs with the same data, the new model yields much smaller values of Akaike information criterion and corrected Akaike information criterion. We compared the predicted rates of earthquakes at the epicentres just before the related earthquakes with the mean spatial seismic rate. Among all 37 earthquakes in the expanded catalogue, the epicentres of 21 earthquakes are located in the regions of higher rates.

### Keolu Fox

*University of San Diego*

Date: Wednesday 1 February 2017

*Keolu has a strong background in using genomic technologies to understand human variation and disease. Throughout his career he has made it his priority to focus on the interface of minority health and genomic technologies. Keolu earned a Ph.D. in Debbie Nickerson's lab in the University of Washington's Department of Genome Sciences (August, 2016). In collaboration with experts at Bloodworks Northwest, (Seattle, WA) he focused on the application of next-generation genome sequencing to increase compatibility for blood transfusion therapy and organ transplantation. Currently Keolu is a postdoc in Alan Saltiel's lab at the University of California San Diego (UCSD) School of Medicine, Division of Endocrinology and Metabolism and the Institute for Diabetes and Metabolic Health. His current project focuses on using genome editing technologies to investigate the molecular events involved in chronic inflammatory states resulting in obesity and catecholamine resistance.*

### Roy Costilla

*Victoria University Wellington*

Date: Tuesday 24 January 2017

Despite its name however, BNP models are actually massively parametric. A parametric model uses a function with finite dimensional parameter vector as prior. Bayesian inference then proceeds to approximate the posterior of these parameters given the observed data. In contrast to that, a BNP model is defined on an infinite dimensional probability space thanks to the use of a stochastic process as a prior. In other words, the prior for a BNP model is a space of functions with an infinite dimensional parameter vector. Therefore, instead of avoiding parametric forms, BNP inference uses a large number of them to gain more flexibility.

To illustrate this, we present simulations and also a case study where we use life satisfaction in NZ over 2009-2013. We estimate the models using a finite Dirichlet Process Mixture (DPM) prior. We show that this BNP model is tractable, i.e. is easily computed using Markov Chain Monte Carlo (MCMC) methods; allowing us to handle data with big sample sizes and estimate correctly the model parameters. Coupled with a post-hoc clustering of the DPM locations, the BNP model also allows an approximation of the number of mixture components, a very important parameter in mixture modelling.

### Jorge Navarro Alberto

*Universidad Autónoma de Yucatán (UADY)*

Date: Wednesday 9 November 2016

**NOTE day and time of this seminar**

The subject of the talk is statistical methods (both theoretical and applied) and computational algorithms for the analysis of binary data, which have been applied in ecology in the study of species composition in systems of patches with the ultimate goal to uncover ecological patterns. As a starting point, I review Gotelli and Ulrich's (2012) six statistical challenges in null model analysis in Ecology. Then, I exemplify the most recent research carried out by me and other statisticians and ecologists to face those challenges, and applications of the algorithms outside the biological sciences. Several topics of research are proposed, seeking to motivate statisticians and computer scientists to venture and, eventually, to specialize in the subject of the analysis of co-occurrences.

Reference: Gotelli, NJ and Ulrich, W, 2012. Statistical challenges in null model analysis.

*Oikos*121: 171-180

### Scotland Leman

*Virginia Tech, USA*

Date: Tuesday 8 November 2016

**NOTE day and time of this seminar**

In this talk I will primarily discuss the Multiset Sampler (MSS): a general ensemble based Markov Chain Monte Carlo (MCMC) method for sampling from complicated stochastic models. After which, I will briefly introduce the audience to my interactive visual analytics based research.

Proposal distributions for complex structures are essential for virtually all MCMC sampling methods. However, such proposal distributions are difficult to construct so that their probability distribution match that of the true target distribution, in turn hampering the efficiency of the overall MCMC scheme. The MSS entails sampling from an augmented distribution that has more desirable mixing properties than the original target model, while utilizing a simple independent proposal distributions that are easily tuned. I will discuss applications of the MSS for sampling from tree based models (e.g. Bayesian CART; phylogenetic models), and for general model selection, model averaging and predictive sampling.

In the final 10 minutes of the presentation I will discuss my research interests in interactive visual analytics and the Visual To Parametric Interaction (V2PI) paradigm. I'll discuss the general concepts in V2PI with an application of Multidimensional Scaling, its technical merits, and the integration of such concepts into core statistics undergraduate and graduate programs.

### Ivor Cribben

*University of Alberta*

Date: Wednesday 19 October 2016

**NOTE day and time of this seminar**

Spectral clustering is a computationally feasible and model-free method widely used in the identification of communities in networks. We introduce a data-driven method, namely Network Change Points Detection (NCPD), which detects change points in the network structure of a multivariate time series, with each component of the time series represented by a node in the network. Spectral clustering allows us to consider high dimensional time series where the number of time series is greater than the number of time points. NCPD allows for estimation of both the time of change in the network structure and the graph between each pair of change points, without prior knowledge of the number or location of the change points. Permutation and bootstrapping methods are used to perform inference on the change points. NCPD is applied to various simulated high dimensional data sets as well as to a resting state functional magnetic resonance imaging (fMRI) data set. The new methodology also allows us to identify common functional states across subjects and groups. Extensions of the method are also discussed. Finally, the method promises to offer a deep insight into the large-scale characterisations and dynamics of the brain.

### John Tipton

*Colorado State University*

Date: Tuesday 18 October 2016

**NOTE day and time of this seminar**

Many scientific disciplines have strong traditions of developing models to approximate nature. Traditionally, statistical models have not included scientific models and have instead focused on regression methods that exploit correlation structures in data. The development of Bayesian methods has generated many examples of forward models that bridge the gap between scientific and statistical disciplines. The ability to fit forward models using Bayesian methods has generated interest in paleoclimate reconstructions, but there are many challenges in model construction and estimation that remain.

I will present two statistical reconstructions of climate variables using paleoclimate proxy data. The first example is a joint reconstruction of temperature and precipitation from tree rings using a mechanistic process model. The second reconstruction uses microbial species assemblage data to predict peat bog water table depth. I validate predictive skill using proper scoring rules in simulation experiments, providing justification for the empirical reconstruction. Results show forward models that leverage scientific knowledge can improve paleoclimate reconstruction skill and increase understanding of the latent natural processes.

### Benjamin Fitzpatrick

*Queensland University of Technology*

Date: Monday 17 October 2016

**NOTE day and time of this seminar**

When making inferences concerning the environment, ground truthed data will frequently be available as point referenced (geostatistical) observations accompanied by a rich ensemble of potentially relevant remotely sensed and in-situ observations.

Modern soil mapping is one such example characterised by the need to interpolate geostatistical observations from soil cores and the availability of data on large numbers of environmental characteristics for consideration as covariates to aid this interpolation.

In this talk I will outline my application of Least Absolute Shrinkage Selection Opperator (LASSO) regularized multiple linear regression (MLR) to build models for predicting full cover maps of soil carbon when the number of potential covariates greatly exceeds the number of observations available (the p > n or ultrahigh dimensional scenario). I will outline how I have applied LASSO regularized MLR models to data from multiple (geographic) sites and discuss investigations into treatments of site membership in models and the geographic transferability of models developed. I will also present novel visualisations of the results of ultrahigh dimensional variable selection and briefly outline some related work in ground cover classification from remotely sensed imagery.

Key references:

Fitzpatrick, B. R., Lamb, D. W., & Mengersen, K. (2016). Ultrahigh Dimensional Variable Selection for Interpolation of Point Referenced Spatial Data: A Digital Soil Mapping Case Study.

*PLoS ONE*, 11(9): e0162489.

Fitzpatrick, B. R., Lamb, D. W., & Mengersen, K. (2016). Assessing Site Effects and Geographic Transferability when Interpolating Point Referenced Spatial Data: A Digital Soil Mapping Case Study. https://arxiv.org/abs/1608.00086

### Paul van Dam-Bates

*Department of Conservation*

Date: Thursday 13 October 2016

Authors: Paul van Dam-Bates[1], Ollie Gansell[1] and Blair Roberston[2]

1 Department of Conservation, New Zealand

2 University of Canterbury, Department of Mathematics and Statistics

### Murray Efford

*Department of Mathematics and Statistics*

Date: Thursday 6 October 2016

### Jimmy Zeng

*Department of Preventive and Social Medicine*

Date: Thursday 29 September 2016

### Richard Barker

*Department of Mathematics and Statistics*

Date: Thursday 22 September 2016

*N*and detectability

*p*. They are popular because they allow inference about

*N*while controlling for factors that influence

*p*without the need for marking animals. Using a capture-recapture perspective we show that the loss of information that results from not marking animals is critical, making reliable statistical modeling of

*N*and

*p*problematic using just count data. We are unable to fit a model in which the detection probabilities are distinct among repeat visits as this model is overspecified. This makes uncontrolled variation in

*p*problematic. By counter example we show that even if

*p*is constant after adjusting for covariate effects (the 'constant

*p*' assumption) scientifically plausible alternative models in which

*N*(or its expectation) is non-identifiable or does not even exist, lead to data that are practically indistinguishable from data generated under an N-mixture model. This is particularly the case for sparse data as is commonly seen in applications. We conclude that under the constant

*p*assumption reliable inference is only possible for relative abundance in the absence of questionable and/or untestable assumptions or with better quality data then seen in typical applications. Relative abundance models for counts can be readily fitted using Poisson regression in standard software such as R and are sufficiently flexible to allow controlling for

*p*through the use covariates while simultaneously modeling variation in relative abundance. If users require estimates of absolute abundance they should collect auxiliary data that help with estimation of

*p*.

### Mohammad Ali Nilforooshan

*Department of Mathematics and Statistics*

Date: Thursday 15 September 2016

### Katrina Sharples

*Department of Mathematics and Statistics*

Date: Thursday 8 September 2016

### Sander Greenland

*University of California*

Date: Monday 5 September 2016

**Note day, time and venue of this special seminar**

Sander Greenland is Research Professor and Emeritus Professor of Epidemiology and Statistics at the University of California, Los Angeles. He is a leading contributor to epidemiological statistics, theory, and methods, with a focus on the limitations and misuse of statistical methods in observational studies. He has authored or co-authored over 400 articles and book chapters in epidemiology, statistics, and medical publications, and co-authored the textbook Modern Epidemiology.

Professor Greenland has played an important role in the recent discussion following the American Statistical Association’s statement on the use of p values.[1-3] He will discuss lessons he took away from the process and how they apply to properly interpreting what is ubiquitous but rarely interpreted correctly by researchers: Statistical tests, P-values, power, and confidence intervals.

1. Ronald L. Wasserstein & Nicole A. Lazar (2016): The ASA's statement on p-values: context, process, and purpose,

*The American Statistician*, 70, 129-133, DOI: 10.1080/00031305.2016.1154108

2. Greenland, S., Senn, S.J., Rothman, K.J., Carlin, J.C., Poole, C., Goodman, S.N., and Altman, D.G. (2016). Statistical tests, confidence intervals, and power: A guide to misinterpretations.

*The American Statistician*, 70, online supplement 1 at http://www.tandfonline.com/doi/ suppl/10.1080/00031305.2016.1154108; reprinted in the European Journal of Epidemiology, 31, 337-350.

3. Greenland, S. (2016). The ASA guidelines and null bias in current teaching and practice.

*The American Statistician*, 70, online supplement 10 at http://www.tandfonline.com/doi/ suppl/10.1080/00031305.2016.1154108

### Mei Peng

*Department of Food Science*

Date: Thursday 25 August 2016

Answers to these questions have a close relevance to illuminating the sensory physiology of sugar metabolism, as well as to practical research of sucrose substitution. In this seminar, I would like to present findings from a few behavioural experiments focused on inter-individual and intra-individual differences in responding to common sugars, using methods from sensory science and cognitive psychology. Overall, our findings challenged some of the conventional beliefs about sweetness perception, and provided some insights into future research about sugar.

### Scott Sisson

*University of New South Wales*

Date: Thursday 18 August 2016

### Ken Dodds

*AgResearch*

Date: Thursday 11 August 2016

__should__we do about it?

### Jeff Miller

*Department of Psychology*

Date: Thursday 4 August 2016

### Jon Waters

*Department of Zoology*

Date: Thursday 28 July 2016

### Tim Jowett

*Department of Mathematics and Statistics*

Date: Thursday 21 July 2016

### Phil Wilcox

*Department of Mathematics and Statistics*

Date: Thursday 14 July 2016

### Honours and PGDip students

*Department of Mathematics and Statistics*

Date: Friday 27 May 2016

Michel de Lange :

*Deep learning*

Georgia Anderson :

*Probabilistic linear discriminant analysis*

Nick Gelling :

*Automatic differentiation in R*

15-MINUTE BREAK 2.40-2.55

MATHEMATICS

Alex Blennerhassett :

*Toeplitz algebra of a directed graph*

Zoe Luo :

*Wavelet models for evolutionary distance*

Xueyao Lu :

*Making sense of the λ-coalescent*

Terry Collins-Hawkins :

*Reactive diffusion in systems with memory*

Josh Ritchie :

*Linearisation of hyperbolic constraint equations*

**Also**

CJ Marland :

*Extending matchings of graphs: a survey*

This one mathematics project presentation takes place at 12 noon on Thursday 26 May, room 241

### Trent Smith

*Department of Economics*

Date: Thursday 26 May 2016

### Jerome Cao

*Department of Mathematics and Statistics*

Date: Thursday 19 May 2016

### Richard Vale

*WRUG*

Date: Tuesday 17 May 2016

### Stephen Duffull

*School of Pharmacy*

Date: Thursday 12 May 2016

### Beatrix Jones

*Massey University*

Date: Thursday 5 May 2016

Joint work with Gideon Bistricer (Massey Honors Student) Carlos Carvalho (U Texas) and Richard Hahn (U Chicago)

### Ben Daniel

*HEDC*

Date: Thursday 28 April 2016

In this seminar, I will first present findings from a large scale research project examining the concept of research methodology among academic staff involved in teaching methods courses from 139 universities in 9 countries. I will then discuss how this has ultimately influenced the way academics relate to and approach teaching of the subject.

Secondly, I will share key findings from another study aimed at exploring postgraduate students’ views on the value of research methodology and outline the challenges they face in learning the subject. To conclude, I will address the question whether the recognition of research methodology as an independent field of study within data science can contribute to better understanding of current and future challenges associated with the increasing availability of data from vast interconnected and loosely coupled systems within the higher education sector.

### Chuen Yen Hong

*Department of Mathematics and Statistics*

Date: Thursday 21 April 2016

### Benoit Auvray

*Department of Mathematics and Statistics*

Date: Thursday 14 April 2016

In this seminar, we will present the work undertaken by our group and compare it with the existing genetic evaluation system.

### Will Probert

*University of Nottingham*

Date: Thursday 7 April 2016

We examine this trade-off using data from the UK 2001 outbreak of foot-and-mouth disease (FMD) by fitting a dynamic epidemic model to the observed infection data available at several points throughout each outbreak and compare forward simulations of the impact of alternative culling and vaccination interventions. For comparison, we repeat these forward simulations at each time point using the model fitted to data from the complete outbreak.

Results illustrate the impact of the accrual of knowledge on both model predictions and on the evaluation of candidate control actions, and highlight the importance of control policies that permit both rapid response and adaptive updating of control actions in response to additional information.

### Genevera Allen

*Rice University, Texas*

Date: Thursday 24 March 2016

Joint work with Eunho Yang, Pradeep Raviukmar, Zhandong Liu, Yulia Baker, and Ying-Wooi Wan

### Kate Lee

*Auckland University of Technology*

Date: Wednesday 23 March 2016

**Note day and time; not the usual**

The goal of Bayesian inference is to infer a parameter and a model in a Bayesian setup. In this talk I will discuss some well-known problems in finite mixture and extreme modelling, and I will present my recent work.

Finite mixture model is a flexible tool for modelling multimodal data and has been used in many applications in statistical analysis. The model evidence is often approximated and it makes demands on computation due to a well-known lack of identifiability. I will present the dual importance sampling scheme to fit the demand of evidence approximation and show how to reduce the computational workload. Lastly, an extreme event is often described by modelling exceedances over the threshold and the threshold value plays a key role in the statistical inference. I will demonstrate that a suitable threshold can be determined using the Bayesian measure of surprise and this approach is easily implemented for both univariate and multivariate extremes.