Georgi Boshnakov - School of Mathematics
Table of Contents
Dr. Georgi Boshnakov
Lecturer in Statistics
Tel.:+44 (0)161 306 3684
Fax:+44 (0)161 275 5819
Alan Turing Building/1.125
Time series analysis and forecasting, Data analysis, Sports modelling, Statistical computing, Matrices in statistics, Symbolic computation.
Packages and other resources created by me (some with collaborators).
(this is for my R packages, for general R resources see section R - information and resources).
Contributions to TeX/LaTeX.
Support for typesetting and Bulgarian and other information about Emacs.
You may be asked for identification to get access to some of the pages below.
A collection of resources about R. The selection is somewhat taylored to the courses I teach.
I mainly offer projects which fall under the umbrella of
time series analysis,
dynamic models (linear and non-linear) and prediction (especially
distributional and probability forecasting). Application areas change over time and sometimes
are influenced by recent industrial consultancies or similar involvements. One such
area on which I am currently actively working is the development of dynamic models for
prediction of sporting events (including in-play). For other topics in statistics and
probability (usually motivated by my research on time series) see my recent publications and
Strong mathematical and computational background is required.
Recent Ph.D. students
- Bisher Iqelan
- Sourav Das (joint with T. Subba Rao)
- A B M Shahadat Hossain
- Mary Akinyemi
- Lina Hamadeh
- Tarak Kharrat
- Jamie Halliday
I offer mainly projects on topics in time series and statistical computing. The projects require background in time series and in most cases this means that students should do (or have done) a time series or similar course.
MMath students wishing to do such projects in their 4th year will improve their prospects by planning ahead and taking the time series course (MATH38032 or MATH48032) in their 3rd year. Strong students are encouraged to contact me about projects related to my research.
1st and 2nd year students reading this section and planning their options should notice that the Statistical methods course in 2nd Sem/2nd year is a prerequisite for (essentially) all subsequent statistics courses. Practical statistics I is a big plus too.
Recent workshops co-organised by me: