Luke Mosley

Luke Mosley

Luke Mosley



I am currently a PhD student at Lancaster University working on high-dimensional statistics and the disaggregation of time series . This work is in joint collaboration with the STOR-i Centre of Doctoral Training at Lancaster University and the Office for National Statistics. Before this, I completed a Masters in Statistics and Operation Research with STOR-i CDT gaining an MRes with distinction. My undergraduate degree was completed at Warwick University gaining a BSc(Hons) in Mathematics and Statistics. My research interests lie in the crossover between high-dimensional statistics and time series analysis, in particular, how we can fuse statistical learning concepts into the econometrics literature to help disaggregate official time series output.

Alongside my PhD I am a Graduate Teaching Assistant at Lancaster University teaching a range of modules including 1st year Maths modules such as Calculus, Probability and Linear Algebra, and 4th year Masters modules including Statistical Learning. Further to this, I have experienced teaching Maths and English to a much younger age group at Explore Learning as a part-time tutor there during my time at Warwick.

Away from studies, my two main passions are sport and music. Football is the main sport I have played for the majority of my life, currently representing the Graduate College team at Lancaster. I am also a keen cricketer being lucky enough to represent both Wigan District and Greater Manchester West during secondary school. In my spare time, I like to challenge myself to golf and snooker, both being frustratingly difficult. I am a big fan of rock and roll and love going to gigs and festivals.

Personal Information

  • NameLuke Mosley
  • Age24 Years
  • ResidenceLancaster, UK
  • OccupationPhD Student
  • UniversityLancaster


  • PhD

    STOR-i CDT, Lancaster University and The Office for National Statistics

    Working with the Office for National Statistics to develop novel methods for combining insight from panel data, and higher-resolution observational time-series, with the goal to meet the ever-increasing user demand for improved and more detailed statistics.

    Present Oct 2019
  • MRes, Statistics and Operational research

    STOR-i CDT, Lancaster University

    Undertaking a Masters of Research in statistics and operational research, giving me an overview of thriving research areas and an opportunity to develop a formal research proposal for my PhD.

    Sept 2019 Oct 2018
  • BSc (Hons), Mathematics and Statistics

    Warwick University

    Provided me with a thorough grounding in both theoretical and practical aspects of modern statistics.

    July 2018 Oct 2015

Work Experience

  • Graduate Teaching Assistant

    Lancaster University

    Workshop tutor for the MATH100 series including: Calculus, Further Calculus, Probability, Statistics and Linear Algebra. Also, the workshop tutor for CFAS420 - Statistical Learning.

    Present Oct 2019
  • Temp leasehold administrator

    Onward Homes Ltd, Bolton

    My role involved carrying out administration duties for leasehold departments and going through a number of confidential and sensitive leaseholder files; scanning needed documents and destroying unneeded ones.

    Sep 2018 July 2018
  • Tutor

    Explore Learning, Leamington Spa

    My goal was to transfer a true passion for education, trying to motivate and inspire children from 4 to 14 to become confident learners in English and Mathematics.

    Feb 2018 Feb 2017
  • Waiter and bar man

    Tavernfayre, Bolton

    Maximise customer satisfaction by being energetic, quick to respond to problems and creating a welcoming atmosphere for customers.

    Jan 2017 Jan 2014



What is STOR-i?

The STOR-i Centre for Doctoral Training, a joint venture between the departments of Mathematics and Statistics and Management Science, offers a four-year PhD programme in Statistics and Operational Research (STOR) developed and delivered with industrial partners. STOR-i was established in 2010 as an EPSRC Centre for Doctoral Training. The Centre has developed an international reputation for the quality of its research. The multi-million pound award includes substantial investment from EPSRC, Lancaster University and several key industrial partners.

The programme

The training programme spans four years, consisting of a foundation year resulting in the award of a Masters of Research (MRes), followed by a three year period of study leading to a PhD. The first year includes taught courses, projects and group activities providing a grounding in Statistics and Operational Research, an overview of thriving research areas, and an opportunity to develop a formal research proposal for a PhD. In years 2-4 involves the completion of a PhD project, encountering real-life commercial challenges, developing leading-edge STOR research and making a real impact on major industrial and scientific applications.

To visit the STOR-i website, please click here.

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PhD Project:
High-Dimensional Disaggregation of Time Series satisfying both Temporal and Contemporaneous Constraints
STOR-i Centre for Doctoral Training at Lancaster University and the Office for National Statistics
Professor Idris Eckley and Dr Alex Gibberd (Maths and Stats, Lancaster) and Duncan Elliott (Office for National Statistics)

Paper 1 (2021): Sparse Temporal Disaggregation
Abstract: Temporal disaggregation is a method commonly used in official statistics to enable high-frequency estimates of key economic indicators, such as GDP. Traditionally, such methods have relied on only a couple of high-frequency indicator series to produce estimates. However, the prevalence of large, and increasing, volumes of administrative and alternative data-sources motivates the need for such methods to be adapted for high-dimensional settings. In this article, we propose a novel sparse temporal disaggregation procedure and contrast this with the classical Chow-Lin method. We demonstrate the performance of our proposed method through simulation study, highlighting various advantages realised. We also explore its application to disaggregation of UK gross domestic product data, demonstrating the method’s ability to operate when the number of potential indicators is greater than the number of low-frequency observations.
ArXiv Link:
Paper 2 (Current Work): Multivariate Temporal Disaggregation with Contemporaneous Constraints
Abstract: In economics, governments want to produce high-frequency information of regions in a nation to better understand regional disparity, deviations from the national avergae and regional correlation. This high-frequency information must agree both temporally with current low-frequency information and contemporaneously with national accounts. In hydrology, efforts are required to produce rainfall information at a more granular level, agreeing with the wider observed spatial coverage. In this work, we build a regularised multivariate regression model with a vector auto-regressive (VAR) error structure to produce high-frequency estimates of multiple time series that aggregtae to temporal and contemporaneous restrctions. The assumption of VAR errors allows one to model the correlation dynamics between regions in a data-based way. We incorporate regularisation techniques to estimate the large, sparse covariance matrix and allow for high-dimensional indicator sets to be used. We apply our method to annual UK NUTS 1 level regional GDP to produce quartery figures using a large indicator set. In addition, we perform a hydrology case study to disaggregate rainfall data.
R Package (Current Work): DisaggregateTS
We are in the process of building an easy-to-use R package that allows both univariate and multivariate disaggregation of time series. We make some of the well-established methods to do temporal disaggregation available, as well as the two methods we proposed in papers 1 and 2. These novel methods allow the user to include as many indicator series as they wish to perform the disaggregation task and an assessment of indicator relevance is returned. They also allow the user to perform a multivariate disaggregation of multiple time series that must satisfy contemporaneous constraints. Appealing graphical visualitions will be availale for the outputs of multiple time series. Real data sets will be available for the user to test out each method.


The MRes component of the STOR-i programme includes taught courses, projects and group activities providing me with a grounding in statistics and operational research, an overview of thriving research areas, and an opportunity to develop a formal research proposal for my PhD. Below is a list of project reports I have completed this year. Also, check out my blog to read about some of the research areas I have been exposed to.

16/04/2019: Using adaptive random search for simulation optimisation - POSTER

31/03/2019: Modelling the demand of healthcare systems using infinite-server queueing models

24/02/2019: Detecting multiple changes in variance of univariate time series data

03/02/2019: Continuous-Time Markov chains and Discrete-Time Markov decision processes

03/12/2018: Branch and Bound algorithm to solve the Knapsack problem


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