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Statistics at Kent

Three bitcoins
Three bitcoins (an alternative 'universal' currency) . They are gold-colored bitcoins on a black surface. Image Source: Dmitry Moraine, www.unsplash.com

Recent grants

Recent grants received by members of the Statistics group have included:

Modelling removal and re-introduction data for improved conservation

Awarded December 2018

Principal Investigator: Rachel McCrea

EPSRC New Investigator Award EP/S020470/1

Description: Optimising the study design of conservation monitoring schemes helps to make the most of limited time and funding. Developing robust approaches for both removal and re-introduction programmes will allow resources to be allocated optimally, balancing the need to allow sufficient time while minimising risk to the species under study. This project will develop new statistical approaches and address how to optimise study design given the constraints of time and cost. The possibilities for automating some parts of procedures, and developing bespoke web applications to support this, will also be investigated.

Development of multiphase flow measurement technology through soft computing

03/2018-02/2020

Academic Supervisor: Dr Xue Wang

Funders: Technology and Strategy Board (TSB, 50%) and Engineering and Physical Sciences Research Council (EPSRC, 50%)

Description: Controlling process flows precisely is critical to specialised industrial processes. Soft computing methodologies offer ways of balancing the trade-offs between, for example, tolerance for imprecision, costs of implementation, and robustness. A collaboration between SMSAS, the School of Electronic and Digital Arts, and KROHNE Ltd, the funding received was £169,772.

Bayesian methods in support of risk management and asset pricing of large stock portfolios

03/2016 – 03/2017

Principal InvestigatorDr Cristiano Villa

FunderRoyal Society

Description: Several problems in finance revolve around the tails of a distribution and the uncertainty about its shape. To develop effective risk management solutions and asset pricing methods it is important to consider the uncertainty carried by the limited amount of information available. To achieve this, we propose to use an objective Bayesian approach to directly capture the tail uncertainty, by comparing tail behavior of hundreds of stocks to identify similarities and differences.

Empirical and bootstrap likelihood procedures for approximate Bayesian inference

2015 – 2017

Principal InvestigatorDr Fabrizio Leisen

Co-Investigators: Prof Brunero Liseo/Dr Clara Grazian (University of Rome La Sapienza)

FunderRoyal Society International Exchanges

Description: The overall scientific aim of this project is to provide new methods to analyse statistical models from a Bayesian perspective. In particular, we will focus on situations where the likelihood is intractable due to analytical or computational complexity. From the methodological point of view, we will tackle the problem with Empirical and Bootstrap Likelihood procedures. In particular, we will explore the new methodology in some specific challenging situations, where the orthodox Bayesian way is out of reach. The new findings will be tested on several datasets.

Flexible Bayesian non-parametric priors

2014 – 2018

Principal InvestigatorDr Fabrizio Leisen

Marie Curie Career Integration Grant FP7- 630677

Description: The use of Bayesian non-parametric (BNP) priors in applied statistical modeling has become increasingly popular in the last few years. From the seminal paper of Ferguson (1973, Annals of Statistics), the Dirichlet Process and its extensions have been increasingly used to address inferential problems in many fields. Examples range from variable selection in genetics to linguistics, psychology, human learning, image segmentation, and applications to the neurosciences. The aim of the project is to provide new flexible BNP priors for modelling different situations. In particular, two research lines will be investigated: vectors of BNP priors and non exchangeable species sampling sequences.

CO2 Flow Metering through Multi-Modal Sensing and Statistical Data Fusion

2014-2016

Co-Investigator: Dr Xue Wang

Funders: EPSRC and the Department of Energy and Climate Change (DECC)

UKCCSRC-C2-C218