Multivariate Count Autoregressive Models and their Assessment
Lancaster University, Department of Mathematics & Statistics
Funding is available for either a 1+3 (MSc+PhD) or +3 (PhD) studentship between the Department of Mathematics & Statistics at Lancaster University and our industry partner ONS, funded by the ESRC – Northwest Social Science Doctoral Training Partnership.
The studentship includes payment of home tuition fees and an annual maintenance grant of £14,777 (tax-free) together with an advanced quantitative methods enhancement of an extra £3000 for the three years of the PhD. The start date is 1st October 2019. The project will be supervised by Prof Konstantinos Fokianos and Dr Rebecca Killick from the Department of Mathematics & Statistics, Lancaster University and Duncan Elliott from the Office for National Statistics.
Description of the project:
Count time series data are found in diverse applications arising in the field of Economic and Social Statistics. For example, the Office of National Statistics (ONS), publish time series data sets on business activity and demographics, labour market status, people and population, and so on. Much of these data are multivariate integer-valued time series that consist of counts and there is considerable lack of methodology for their proper analysis. Moreover, new data sources to integrate with or replace traditional surveys are being explored by ONS and the wider Government Statistical Service to improve understanding of the UK’s economy, society and population, with time series analysis playing an important role.
This work will deliver new statistical methods for the analysis of multivariate count time series. Emphasis will be given to develop multivariate models which take into account overdispersion and include important covariates, like autoregressive values and a trend or/and periodic component. Besides building new models that are relevant to ONS data analysis, we will also deliver diagnostics for these models to ensure they are applicable by practitioners without requiring expert knowledge. The development of an open source R-package will make available our methodological work to a wide audience
The PhD project will require the person to visit the ONS offices in Newport, Wales for approximately 4 visits of five days each per year.
Academic Requirements: A first-class degree (or equivalent) for MSc+PhD and a good Master’s degree (or equivalent) for the PhD in an appropriate subject.
How to apply: Please send a cover letter, CV and undergraduate/Masters transcript(s) to [log in to unmask] The cover letter should clearly explain your motivation for applying for this specific studentship. More details can be found under https://www.lancaster.ac.uk/maths/study/phd/ Applications will be considered as they are received.
Final deadline: 28th February 2019
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