##
Similar case studies
**
**

### REF impact found *
67* Case Studies

#### Currently displayed text from case study:
Summary of the impact
Underpinning research
References to the research
Details of the impact
Sources

### C3 - Applications of Bayesian methods in finance, credit scoring and target tracking

**Summary of the impact**

In recent years there has been an explosion of real data from areas as diverse as bioinformatics, genetics, engineering and finance. Coupled with this has been the development of complex and realistic Bayesian statistical models to represent these data. In order to use these models to perform (Bayesian) statistical inference, one is required to calculate integrals, which are unknown analytically. Most of the numerical methods used to approximate these integrals are based upon Monte Carlo methods of which some of the seminal work has been done at Imperial College London, for instance the `particle-filter' developed in 1993 [4]. These methods are now very widely used in finance for automated trading, calculating the probability of default for economies, and for target tracking in the defence sector and we give explicit exemplars of each. The numerical methods developed at Imperial have been important in applying realistic models to these varied application areas and have impacted companies and organisations as diverse as Maple-Leaf Capital LLC, QinetiQ and the Credit Research Initiative.

**Submitting Institution**

Imperial College London**Unit of Assessment**

Mathematical Sciences**Summary Impact Type**

Technological**Research Subject Area(s)**

**Mathematical Sciences:**Statistics

### Improving data analysis via better statistical infrastructure

**Summary of the impact**

A generalized additive model (GAM) explores the extent to which a single output variable of a complex system in a noisy environment can be described by a sum of smooth functions of several input variables.

Bath research has substantially improved the estimation and formulation of GAMs and hence

- driven the wide uptake, outside academia, of generalized additive models,
- increased the scope of applicability of these models.

This improved statistical infrastructure has resulted in improved data
analysis by practitioners in fields such as natural resource management,
energy load prediction, environmental impact assessment, climate policy,
epidemiology, finance and economics. In REF impact terms, such changes in
practice by practitioners leads ultimately to direct economic and societal
benefits, health benefits and policy changes. Below, these impacts are
illustrated via two specific examples: (1) use of the methods by the
energy company EDF for electricity load forecasting and (2) their use in
environmental management. The statistical methods are implemented in *R*
via the software package mgcv, largely written at Bath. As a `recommended'
*R* package mgcv has also contributed to the global growth of *R,*
which currently has an estimated 1.2M business users worldwide [A].

**Submitting Institution**

University of Bath**Unit of Assessment**

Mathematical Sciences**Summary Impact Type**

Economic**Research Subject Area(s)**

**Mathematical Sciences:**Applied Mathematics, Statistics

**Economics:**Econometrics

### 01_Phylogenetic analysis software BEAST informs public health responses to infection.

**Summary of the impact**

**Impact**: BEAST software has widespread applications with impacts on
public health policy, service
provision and awareness, and in other contexts such as commercial disputes
and criminal cases.

**Beneficiaries:** Public agencies such as health bodies and criminal
courts; ultimately, global and
local populations subject to infectious disease epidemic and pandemic
outbreaks in which BEAST
is used to inform the response.

**Significance and Reach:** BEAST is critical software that has been
used to understand the spread
of and to inform the response to global pandemics such as H1N1 swine-flu.
It is also used to
determine disease origin and transmission issues in specific situations
(e.g. in criminal cases). The
reach of this software is therefore both global and local.

**Attribution:** Rambaut (UoE) co-led the phylogenetic research and
developed BEAST with
Drummond (Auckland, NZ). The subsequent epidemic and pandemic analyses
were variously led
by Rambaut and Pybus (Oxford) and by Ferguson (Imperial College London).

**Submitting Institution**

University of Edinburgh**Unit of Assessment**

Biological Sciences**Summary Impact Type**

Technological**Research Subject Area(s)**

**Mathematical Sciences:**Statistics

**Biological Sciences:**Genetics

**Medical and Health Sciences:**Medical Microbiology

### The use of multilevel statistical modelling has led to improved evidence-based policy making in education and other sectors

**Summary of the impact**

Since 2008, statistical research at the University of Bristol has significantly influenced policies, practices and tools aimed at evaluating and promoting the quality of institutional and student learning in the education sector in the UK and internationally. These developments have also spread beyond the education sector and influence the inferential methods employed across government and other sectors. The underpinning research develops methodologies and a much-used suite of associated software packages that allows effective inference from complicated data structures, which are not well-modelled using traditional statistical techniques that assume homogeneity across observational units. The ability to analyse complicated data (such as pupil performance measures when measured alongside school, classroom, context and community factors) has resulted in a significant transformation of government and institutional policies and their practices in the UK, and recommendations in Organisation for Economic Co-operation and Development (OECD) policy documents. These techniques for transforming complex data into useful evidence are well-used across the UK civil service, with consequent policy shifts in areas such as higher education admissions and the REF2014 equality and diversity criteria.

**Submitting Institution**

University of Bristol**Unit of Assessment**

Mathematical Sciences**Summary Impact Type**

Societal**Research Subject Area(s)**

**Mathematical Sciences:**Statistics

**Information and Computing Sciences:**Computation Theory and Mathematics, Information Systems

### Using the data to choose the best model for a statistical analysis, using Reversible Jump Markov chain Monte Carlo: generic model choice for an evidence-informed society

**Summary of the impact**

Reversible Jump Markov chain Monte Carlo, introduced by Peter Green [1] in 1995, was the first generic technique for conducting the computations necessary for joint Bayesian inference about models and their parameters, and it remains by far the most widely used, 18 years after its introduction. The paper has been (by September 2013) cited over 3800 times in the academic literature, according to Google Scholar, the vast majority of the citing articles being outside statistics and mathematics. This case study, however, focusses on substantive applications outside academic research altogether, in the geophysical sciences, ecology and the environment, agriculture, medicine, social science, commerce and engineering.

**Submitting Institution**

University of Bristol**Unit of Assessment**

Mathematical Sciences**Summary Impact Type**

Technological**Research Subject Area(s)**

**Mathematical Sciences:**Statistics

### Statistical methods are helping to control the spread of epidemics

**Summary of the impact**

In a series of papers from 2003, Gibson (Maxwell Institute) and
collaborators developed Bayesian computational methods for fitting
stochastic models for epidemic dynamics. These were subsequently applied
to the design of control programmes for pathogens of humans and plants. A
first application concerns the bacterial infection *Clostridium
difficile* in hospital wards. A stochastic model was developed which
was instrumental in designing control measures, rolled out in 2008 across
NHS Lothian region, and subsequently adopted across NHS Scotland.
Incidence in Lothian reduced by around 65%, saving an estimated £3.5M per
annum in treatment and other costs, reducing mortality and improving
patient outcomes, with similar impacts elsewhere in Scotland. A second
application concerns the spread of epidemics of plant disease in
agricultural, horticultural and natural environments. Models developed in
collaboration with plant scientists from Cambridge have been exploited by
the Department for Environment, Food and Rural Affairs (Defra) and the
Forestry Commission under a £25M scheme, initiated in 2009, to control
sudden oak death in the UK, and by the United States Department of
Agriculture to control sudden oak death in the USA.

**Submitting Institutions**

University of Edinburgh,Heriot-Watt University**Unit of Assessment**

Mathematical Sciences**Summary Impact Type**

Health**Research Subject Area(s)**

**Mathematical Sciences:**Statistics

### Bristolâ€™s research into multiscale methods enables more realistic modelling of real world phenomena providing benefit to industry, government and society.

**Summary of the impact**

Wavelets and multiscale methods were introduced and rapidly became popular in scientific academic communities, particularly mathematical sciences, from the mid-1980s. Wavelets are important because they permit more realistic modelling of many real-world phenomena compared to previous techniques, as well as being fast and efficient. Bristol's research into wavelets started in 1993, has flourished and continues today. Multiscale methods are increasingly employed outside academia. Examples are given here of post-2008 impact in central banking, marketing, finance, R&D in manufacturing industry and commercial software, all originating from research at Bristol. Much of the impact has been generated from the original research via software. This software includes freeware, distributed via international online repositories, and major commercial software, such as Matlab (a preeminent numerical computing environment and programming language with over one million users worldwide).

**Submitting Institution**

University of Bristol**Unit of Assessment**

Mathematical Sciences**Summary Impact Type**

Technological**Research Subject Area(s)**

**Mathematical Sciences:**Statistics

**Information and Computing Sciences:**Computation Theory and Mathematics

**Economics:**Econometrics

### Synthetic weather sequences informing engineering design and supporting decisions about infrastructure

**Summary of the impact**

Research conducted in UCL's Department of Statistical Science has led to the development of a state-of-the-art software package for generating synthetic weather sequences, which has been widely adopted, both in the UK and abroad. The synthetic sequences are used by engineers and policymakers when assessing the effectiveness of potential mitigation and management strategies for weather-related hazards such as floods. In the UK, the software package is used for engineering design; for example, to inform the design of flood defences. In Australia it is being used to inform climate change adaptation strategies. Another significant impact is that UCL's analysis of rainfall trends in southwest Western Australia directly supported the decision of the state's Department of Water to approve the expansion of a seawater desalination plant at a cost of around AUS$450 million. The capacity of the plant was doubled to 100 billion litres per year in January 2013 and it now produces nearly one third of Perth's water supply.

**Submitting Institution**

University College London**Unit of Assessment**

Mathematical Sciences**Summary Impact Type**

Environmental**Research Subject Area(s)**

**Mathematical Sciences:**Statistics

**Earth Sciences:**Atmospheric Sciences

### Development of an innovative data analysis tool to monitor groundwater pollution and environmental impact

**Summary of the impact**

With global demand for energy ever increasing, environmental impact has become a major priority for the oil industry. A collaboration between researchers at the University of Glasgow and Shell Global Solutions has developed GWSDAT (GroundWater Spatiotemporal Data Analysis Tool). This easy-to-use interactive software tool allows users to process and analyse groundwater pollution monitoring data efficiently, enabling Shell to respond quickly to detect and evaluate the effect of a leak or spill. Shell estimates that the savings gained by use of the monitoring tool exceed $10m over the last three years. GWSDAT is currently being used by around 200 consultants across many countries (including the UK, US, Australia and South Africa) with potentially significant impacts on the environment worldwide.

**Submitting Institution**

University of Glasgow**Unit of Assessment**

Mathematical Sciences**Summary Impact Type**

Technological**Research Subject Area(s)**

**Mathematical Sciences:**Statistics

### Improving Barclays Bank's management of its exposure to Counterparty Credit Risk

**Summary of the impact**

In response to the deficiencies in bank risk management revealed following the 2008 financial crisis, one of the mandated requirements under the Basel III regulatory framework is for banks to backtest the internal models they use to price their assets and to calculate how much capital they require should a counterparty default. Qiwei Yao worked with the Quantitative Analyst — Exposure team at Barclays Bank, which is responsible for constructing the Barclays Counterpart Credit Risk (CCR) backtesting methodology. They made use of several statistical methods from Yao's research to construct the newly developed backtesting methodology which is now in operation at Barclays Bank. This puts the CCR assessment and management at Barclays in line with the Basel III regulatory capital framework.

**Submitting Institution**

London School of Economics & Political Science**Unit of Assessment**

Mathematical Sciences**Summary Impact Type**

Economic**Research Subject Area(s)**

**Mathematical Sciences:**Statistics

**Economics:**Applied Economics, Econometrics