Statistics Seminars 2017 - 2018

Statistics Seminars will be in Room E0.01 Science East on Thursdays at 4.00pm.






Title:  Bayesian Nonparametric Modelling of Network Data

Speaker:  Dr. Daniele Durante.
Post-doctoral research fellow in Statistical Science
University of Padova

Date: Thursday, 5th October 2017

Time: 3pm

Location:  Room 1.25, Science Centre North (J. K. Lab)


Many fields of research provide increasingly complex data along with novel motivating applications and new methodological questions. In approaching these data sets it is fundamental to rely on parsimonious representations which make the problem tractable and provide interpretable inference procedures to draw meaningful conclusions. However, in reducing complexity, it is important to avoid restrictive models that lead to inadequate characterization of relevant patterns underlying the observed data. Within this framework, network data representing relationship structures among a set of nodes are a relevant example. Although there has been abundant focus on models for a single network, there is a lack of methods for replicated network-valued data collected from a common population distribution. These data open new avenues for studying underlying connectivity patterns, how they are distributed in the population, and if this distribution changes with predictors of interest. Motivated by neuroscience studies on brain connectivity, I will discuss some issues associated with available statistical models, and I will outline recent methods I proposed to cover some of the current gaps via Bayesian nonparametric models leveraging latent space representations.

Title:        Open and Reproducible Spatial Data Analysis in the Social Sciences – Ideas and Examples.

Speaker:      Prof. Christopher Brunsdon.
Professor of  Geocomputation, and Director of the National Centre for Geocomputation at Maynooth  University

Date:            Thursday,12th October 2017

Time:         3pm

Location:        Room 1.25, Science Centre North

The idea of reproducible research has gained much recent attention.  This is an approach to publishing reports, documents and web sites relating to data analysis in which complete information regarding the data used and the programming scripts used to perform the analysis are encapsulated in a single object.   The idea is that third parties can not only read the report but they can also reproduce any analytical results or visualisations included in the report.  This allows the scrutiny of methods used, as well as the adaptation of methods for different data sets or similar but distinct statistical analyses.

In this talk the key ideas and justifications for reproducible research will be discussed, together with a description of a practical implementation of a reproducible research framework based on the R programming language, together with RStudio and RMarkdown.   In addition to this, some examples of ongoing work using a reproducible paradigm will be given, including an open and reproducible geodemographic classification for the Republic of Ireland,   and the production of tutorial materials for Bayesian spatial modelling using the STAN package.


Title:    Cost-effectiveness modelling to inform resource allocation decision-making in healthcare: Overview and some interesting examples

Speakers:           Dr Ronan Mahon       Team Lead - Health Economic Modelling, Novartis  Dr Andrii Danyliv Senior Health Economic Modelling Manager, Novartis

Date:        Thursday,26th October 2017

Time:         3pm

Location:        Room 1.25, O’Brien Centre for Science (North)

Healthcare system decision-makers are faced with unavoidable resource allocation decisions. In order to make decisions rationally and consistently, an evaluation framework has been developed, whereby the incremental benefit of a new health intervention is weighed against its opportunity cost (i.e. the benefit forgone elsewhere when an investment is made). In particular, cost-effectiveness models are used to estimate the incremental benefit and incremental cost associated with the new treatment. Such cost-effectiveness models have become increasingly mathematically complex and employ techniques such as Markov modelling and survival analysis in order to simulate disease progression. In this presentation, we give an overview of the field and focus on the use of survival analysis in cost-effectiveness modelling, which we hope will be of interest to those in the field of Actuarial Science.


Title:        Polynomial correlations
(joint work with A.J. McNeil  (York) and J. Nešlehová (McGill) ).

Speakers:      Andrew D. Smith HFIA School of Mathematics and Statistics. UCD.

Date:            Thursday, 2nd November 2017

Time:         3pm

Location:        Room 1.25, O’Brien Centre for Science (North)


Mathematically, the correlation of a bivariate random variable is the expected product of two polynomials. These polynomials are the first of two respective polynomial sequences, each of which is an orthonormal sequence with respect to the marginal distributions. This paper shows how expected products of higher order orthonormal polynomials can capture further aspects of dependence. These include convexity, the tendency for high values of one variable to be associated with extreme (high or low) values of another, and arachnitude which is the tendency of extreme (high or low) values of two variables to occur simultaneously We also develop rank versions of these quantities. Alongside the familiar rank correlation, we have rank convexity and rank arachnitude. Sample versions of these statistics can be useful in the calibration of copulas.


Title:        What role for volcanoes in 21st Century climate?

Speakers:     Prof. Peter Thorne.    Director, Irish Climate Analysis and Research Units Department of Geography, Maynooth University

Date:        Thursday, 9th November 2017

Time:         3pm

Location:        Room 1.25, O’Brien Centre for Science (North)


Volcanoes are the wildcard of climate. We know they shall occur and that they have potentially substantial impacts. But, how do we plan for events that are inherently unpredictable, at least in the specifics? This talk shall present a novel approach using a large multi-member ensemble and long ice-core records to attempt to provide a basis for decision makers to incorporate potential volcanic activity into adaptation planning.


Title:          Applied Statistics and Animal Breeding Programmes

Speakers:     Assoc. Professor Alan Fahey School of Agriculture and Food Science, UCD

Date:    Thursday, 16th November 2017

Time:    3pm

Location:    Room 1.25, O’Brien Centre for Science (Nth)


Animal breeding is also known as quantitative genetics or statistical genetics and is used by agriculture livestock industries to exploit the genetic diversity of economically important traits. Animal Breeding is based on the infinitesimal model which assumes that traits are controlled by alleles at an infinite number of loci, and these loci have an equal but small effect on a quantitative trait. Therefore, animal breeders pay little attention to individual genes associated with traits. The main objective of an applied animal breeding selection programme is to cause genetic improvement in traits that move the population towards a predefined breed goal. To do this, genetic evaluations must be conducted to determine the genetic merit of each animal in the population. The genetic merit of an animal is known as the estimated breeding value (EBV) and half of this value is transmitted to the animal’s progeny as each animal contributes 50% of its gene to its progeny. A successful animal breeding programme relies on a successful data collection programme at national and international level. Data is required on the animals’ phenotypes, pedigree, DNA (if available) and farm management data. The phenotype is a combination of its genotype, environment and the interaction between the genotype and the environment. Animal breeders are primarily concerned with estimating the transmittable genotype (additive genotype) and use statistical techniques such as best linear unbiased prediction (BLUP and GBLUP) to simultaneously estimated the genotypic and environmental effects. The variance of the additive genotype is then used to estimate the heritability of the trait, which is the portion of the phenotypic variance that is due to the additive genetic variance. The heritability is then used in the estimation of the breeding value.   The EBVs are used by farmers when making genetic selection decisions.  The profitability of livestock enterprises depends on more than one trait and therefore selection indices have been used to aide in multiple trait selection. These indices consider that traits are not of equal economic important, and that traits in the index can have positive, negative or genetic correlations with each other. Advances in genomic technologies and statistical methodologies haveplayed an important role in the genetic improvement of livestock populations.

 Title:    Spectral Backtests of Forecast Distributions with  Application to Risk Management

Speakers:     Alexander J. McNeil (University of York)     School of Agriculture and Food Science, UCD

Date:        Thursday, 23rd November 2017

Time:     3pm

Location:        Room 1.25, O’Brien Centre for Science (North)

In this talk we study a class of backtests for forecast distributions in which the test statistic  is a spectral transformation that weights exceedance events by a function of the modelled probability level. The choice of the kernel function makes explicit the user's priorities for model performance.  
The class of spectral backtests includes tests of unconditional coverage and tests of conditional coverage. We show how the class embeds a wide variety of backtests in the existing literature, and propose novel variants as well.
We assess the size and power of the backtests in realistic sample sizes, and in particular demonstrate the tradeoff between power and specificity in validating quantile forecasts.