Department of Statistics


Seminars


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Past seminars

Strategic bidding in a discrete accumulating priority queue

Speaker: Raneetha Abeywickrama

Affiliation: University of Auckland

When: Wednesday, 12 April 2017, 3:00 pm to 4:00 pm

Where: 303-310, Room 310, Level 3, Uni building 303, The University of Auckland at 38 Princes Street, Auckland CBD

We consider a single server M/G/1 queue in which customers accumulate priority linearly while waiting. There are a number of priority classes, each of which accumulates priority at a different rate. Upon arrival, each customer pays to enter the queue without knowing the current state of the system. The rate at which they accumulate priority depends on the priority class they have entered. When the server becomes idle, the customer with the greatest accumulated priority is chosen for service. Accumulating priority queues have been proposed in healthcare settings since they permit priority to increase with time spent in the queue. In this talk, we focus on the existence of Nash equilibrium and stability of this model.

Problems with predictive distribution

Speaker: Murray Aitkin

Affiliation: University of Melbourne

When: Wednesday, 12 April 2017, 11:00 am to 12:00 pm

Where: 303-310, Room 310, Level 3, Uni building 303, The University of Auckland at 38 Princes Street, Auckland CBD

Many calls have been made for an increased emphasis on prediction in statistics teaching (see for example Harville 2014). Bayesian prediction has been increasingly popularised through the posterior predictive distribution. This talk raises questions about the interpretation of this distribution, and the need for posterior sampling procedures to provide the uncertainty assessment of predictions.

Harville, D.A. (2014). The need for more emphasis on prediction: a "non-denominational" model-based approach (with discussion). The American Statistician 68 (2), 71-92.

http://www.ms.unimelb.edu.au/~maitkin@unimelb/

Case--control logistic regression is more complicated than you think.

Speaker: Prof. Thomas Lumley

Affiliation: University of Auckland

When: Wednesday, 5 April 2017, 11:00 am to 12:00 pm

Where: 303S-561

It is a truth universally acknowledged that logistic regression gives consistent and fully efficient estimates of the regression parameter under case-control sampling, so we can often ignore the distinction between retrospective and prospective sampling in epidemiology. I will talk about two issues that are more complicated than this. First, the behaviour of pseudo-r^2 statistics under case-control sampling: most of these are not consistently estimated. Second, the question of when and why unweighted logistic regression is much more efficient than survey-weighted logistic regression: the traditional answers of 'always' and 'because of variation in weights' are wrong.

Statistical computing in a (more) static environment
Ross Ihaka & Brendan McArdle

Speaker: Ross Ihaka & Brendan McArdle

Affiliation: Department of Statistics, University of Auckland

When: Wednesday, 29 March 2017, 6:00 pm to 7:30 pm

Where: -

6pm NZT - Foyer area, Ground floor, of Building 302, 23 Symonds Street. Refreshments will be available here before each lecture at 6pm.

6.30pm NZT - Lectures commence at 6.30pm (to 7.30pm), Wednesdays, MLT1 Lecture Theatre, Ground Floor, Building 303, 38 Princes Street.

Or

Live Stream to a screen from 630pm NZT onwards

https://www.stat.auckland.ac.nz/en/about/news-and-events-5/events/events-2017/03/ihaka-ross-ihaka.html

Or join a local group screening in your city.(Auckland, Brisbane, Christchurch, Sydney, Wellington...)

Estimation of a High-Dimensional Covariance Matrix

Speaker: Xiangjie Xue

Affiliation: Uni of Auckland

When: Wednesday, 29 March 2017, 3:00 pm to 4:00 pm

Where: 303-310, Room 310, Level 3, Uni building 303, The University of Auckland at 38 Princes Street, Auckland CBD

The estimation of covariance or precision (inverse covariance) matrices plays a prominent role in multivariate analysis. The usual estimator, the sample covariance matrix, is known to be unstable and ill-conditioned in high-dimensional setting. In the past two decades, various methods have been developed to give a stable and well-conditioned estimator and they have their own advantages and disadvantages. For example, thresholding methods carry almost no computational burden but their estimators can not guarantee to be positive-definite. In this talk, we will review some of the most popular methods and describe a new method to estimate the correlation matrix using the empirical Bayes method. To our best knowledge, we have not yet found any method in the literature using the empirical Bayes method to estimate correlation matrices to date. We use the fact that the elements in the sample correlation matrix can be approximated by the same one-parameter normal distribution with unknown means, along with the non-parametric maximum likelihood estimation proposed by Wang (2007) to give a new estimator of the correlation matrix. Preliminary simulation results show that the new estimator has some advantages over various thresholding methods in estimating sparse covariance matrices.

The Use of Accuracy Indicators in Survey Data Analysis to Compensate for Measurement Error

Speaker: Prof. Skinner,CJ

Affiliation: The London School of Economics and Politics Science

When: Monday, 27 March 2017, 11:00 am to 12:00 pm

Where: 303-310,Room 310, Level 3, Uni building 303, The University of Auckland at 38 Princes Street, Auckland CBD

There is growing interest among survey methodologists in collecting and using auxiliary variables related to the data quality, often called 'paradata'. I shall talk about binary paradata related to measurement error in a survey variable, indicating whether the variable is measured with error. This may be used in the design of the survey instrument. Here, the talk focusses on its use in survey data analysis to correct estimation for the potential biasing effects of measurement error.

http://www.lse.ac.uk/RESEARCHANDEXPERTISE/EXPERTS/profile.aspx?KeyValue=c.j.skinner%40lse.ac.uk

Interactive visualisation and fast computation of the solution path for convex clustering and biclustering
Dr Genevera Allen, Dobelman Family Junior Chair

Speaker: Dr Genevera Allen, Dobelman Family Junior Chair

Affiliation: Departments of Statistics and Electrical and Computer Engineering, Rice University

When: Wednesday, 22 March 2017, 6:00 pm to 7:30 pm

Where: -

6pm NZT - Foyer area, Ground floor, of Building 302, 23 Symonds Street. Refreshments will be available here before each lecture at 6pm.

6.30pm NZT - Lectures commence at 6.30pm (to 7.30pm), Wednesdays, MLT1 Lecture Theatre, Ground Floor, Building 303, 38 Princes Street.

Or

Live Stream to a screen from 630pm NZT onwards

https://www.stat.auckland.ac.nz/en/about/news-and-events-5/events/events-2017/03/ihaka-genevera-allen.html

Or join a local group screening in your city.(Auckland, Brisbane, Christchurch, Sydney, Wellington...)

A Shiny new app for policy makers: Using simulation to test which factors most improve child wellbeing
Barry Milne, Senior Research Fellow and Acting Director

Speaker: Barry Milne, Senior Research Fellow and Acting Director

Affiliation: COMPASS Research Centre

When: Friday, 17 March 2017, 4:00 pm to 5:00 pm

Where: Fale Pasifika Complex (Building 273), Level 1, Room 104

All welcome - Drinks and nibbles to follow.

We have developed an app for policy makers which allows them to test policy scenarios around improving child wellbeing. Designed in the R web application, SHINY, the app allows policy makers and analysts to run realistic simulations in which the effects of changes in children

R and data journalism in New Zealand
Harkanwal Singh

Speaker: Harkanwal Singh

Affiliation: Data Editor, New Zealand Herald

When: Wednesday, 15 March 2017, 6:00 pm to 7:30 pm

Where: -

6pm NZT - Refreshments - Foyer area, Ground floor, of Building 302, 23 Symonds Street. Refreshments will be available here before each lecture at 6pm.

6.30pm NZT - Lectures commence at 6.30pm (to 7.30pm), Wednesdays, MLT1 Lecture Theatre, Ground Floor, Building 303, 38 Princes Street.

Or

Live Stream to a screen from 630pm NZT onwards

https://www.stat.auckland.ac.nz/en/about/news-and-events-5/events/events-2017/03/ihaka-harkanwal-singh.html

Or join a local group screening in your city.(Auckland, Brisbane, Christchurch, Sydney, Wellington...)

Developing modelling competencies in Year 7 and 8 students
Anne Patel

Speaker: Anne Patel

Affiliation: University of Auckland

When: Wednesday, 15 March 2017, 3:00 pm to 4:00 pm

Where: 303-310, Room 310, Level 3, Uni building 303, The University of Auckland at 38 Princes Street, Auckland CBD

Some researchers advocate a statistical modelling approach to inference that draws on students' intuitions about factors influencing phenomena and that requires students to build models. Such a modelling approach to inference became possible with the creation of TinkerPlots Sampler technology. However, little is known about what modelling competencies students need to acquire. Drawing and building on previous research including mathematical modelling research, this study aims to uncover the statistical modelling competencies students need to develop. A design-based research methodology was used. Model Eliciting Activities were developed with a focus on natural variation in an authentic context. Six 11-year-old students working in pairs participated. Camtasia was used to capture students' verbalizations and interactions with TinkerPlots. Pivotal moments in their reasoning were transcribed and analysed alongside written and screen artefacts. The focus of this presentation is on one pair of students as they engaged with a schoolbag weight task. Findings indicate these students seem to be developing the ability to build models, investigate and posit factors, take variation into account and make decisions based on simulated data. From the analysis an initial statistical modelling framework and statistical modelling competency framework are proposed. Implications of the findings and future research plans are discussed.


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