Statistics - Research Internships

Actuarial Science 3rd year students-

I most enjoyed being able to experience what researchers do and understand the process of research. I found it to be “a fun side of studying” in that you actually get to do what you’re interested in.

(I) learnt how to write a paper, contributing to the lit review, excel, references. Learnt about working in a small group – reminding each other, communicating via email, setting up shared documents.

I think the greatest part of the UGRAS is the fact that it provides a supplement to my learning experience which I would have not gotten otherwise. My undergraduate degree does not involve a dissertation and therefore the process of researching and writing a piece of publication was an extremely foreign concept to me.

This research assistantship was a very unique and equally beneficial programme rolled out by the department of statistics. It was a novel opportunity for a fresh graduate to work on cutting edge problems in the practical world, and have a chance to get published as a co-author at such an early stage of my educational career.


















































































George Tzougas.




During 2017 and 2018 I carried out two research projects in collaboration with two 3rd year undergraduate students. Both projects were aimed at supervising and developing an undergraduate student’s interest in research. 

Target audience

Third year undergraduate students.


Both research projects ran from Monday 8th June – Friday 31st August and during that time the two undergraduate research assistants completed 100 hours of paid work. In particular, in 2017 I was very enthusiastic to get involved in the undergraduate research internship scheme which was launched by LSE LIFE and LSE Teaching & Learning Centre. The research paper that the students and I produced in 2017 recently appeared in the internationally recognised European Actuarial Journal, was presented in the Statistics Department in November 2017 and at the 10th International Conference on Computational and Methodological Statistics which was hosted by the University of London in December 2017. The main purpose of this paper was to propose an EM scheme that reduces the computational burden for ML estimation in the Negative Binomial-Inverse Gaussian (NBIG) regression model. The NBIG regression model extended the commonly used specification that assumes that the number of claims is distributed according to a mixed Poisson regression model, which was widely accepted for insurance ratemaking. Furthermore, the project which I did together with two students in 2018, was presented in the Statistics Department in November 2018 and at the 11th International Conference on Computational and Methodological Statistics which was hosted by the University of Pisa in December 2018. The second paper was concerned with presenting the Exponential-Lognormal regression model as a competitive alternative to the Pareto, or Exponential-Inverse Gamma, regression model that has been used in a wide range of areas, including insurance ratemaking. This was the first time that the Exponential-Lognormal regression model was used in a statistical or actuarial context. The main contribution of the study was that we illustrated how maximum likelihood (ML) estimation of the Exponential-Lognormal regression model, which does not have a density in closed form, can be accomplished relatively easily via an Expectation Maximization (EM) type algorithm.

Regarding the application process, students who were interested in applying for one of the 2 available posts were asked to send a CV and cover letter explaining why they were interested and how their current studies / courses made them suitable for the internship.  Indeed, it was very encouraging that we received a high volume of applications. Specifically, we received more than 70 applications from students who were interested in participating in these projects. Due to the high number of applications, a selection procedure was carried out by members of the Department of Statistics, designed so as to identify students more motivated and better prepared for a satisfactory research internship.

Teaching and Research are the most enjoyable aspects of my academic life and through the research internship scheme I would be able to do both during summer time. In this respect, this scheme offered me the opportunity to carry out two research projects through which I could help my students achieve their goals, not only through teaching and by giving them coursework, but also by bringing university research and student education into a symbiotic relationship which can facilitate educationalcreativity growth and innovation. Finally, because my diverse research interests lie on the interface between Statistics, Actuarial science and Computational Statistics I felt that the two research projects I proposed would be of interest to many students at the Department of Statistics since our program provides students an excellent grounding in the application of statistical and actuarial techniques together with a comprehension of fundamental aspects of finance and computational techniques. 


The research internship scheme was an excellent opportunity for 3rd year students to learn new statistical tools, build research skills, learn how to write a research article and provide them with the ability to think in a critical manner by making formal inferences on the basis of real statistical data and other fundamentals upon which they will draw in their professional careers. Finally, especially noteworthy is the outstanding collegiality and support that exists among the Department of Statistics staff’s members, which resulted in bringing both projects into fruition in a timely manner and hence place our Department into an elite group of few Departments in which students can gain a first-hand experience of research by publishing their work in prestigious academic journals. Finally, it is worth mentioning that students return to give a presentation to other students.  This gives other students an insight into the research process and demonstrates how skills they have developed during the programme can be applied.

Next steps

The Department of Statistics values this activity very highly  and a new research project will be carried out in the summer time because the students and I are free from other obligations and thus we will able to meet about three times per week to complete 100 hours of work which also include training at the beginning of the project.  The Department is also planning a summer research project with the Academic Registrar’s Division which will involve analysis of LSE student data.

They should try to predict fairly accurately what the demands of the project would be and what would be required of them and the students. In particular, the planning should be adequate and the breakdown of the tasks that the students will have to do and the amount of time they will have to spend on them should be estimated reasonably accurately. Finally, it should be noted that meeting regularly with the students, at least 3 days per week for about three to five hours each day  was very important because the academic work which I did with them was at PhD level.