# Georgi Boshnakov - School of Mathematics

## Table of Contents

Dr. Georgi Boshnakov
Lecturer in Statistics E-Mail: georgi.boshnakov@manchester.ac.uk Tel.:+44 (0)161 306 3684 Fax:+44 (0)161 275 5819
Alan Turing Building/1.125 |

## Research interests

Time series analysis and forecasting, Data analysis, Sports modelling, Statistical computing, Matrices in statistics, Symbolic computation.

## Publications

## Computing

Packages and other resources created by me (some with collaborators).

### R packages

(this is for my R packages, for general R resources see section R - information and resources).

### LaTeX

Contributions to TeX/LaTeX.

### Emacs

Support for typesetting and Bulgarian and other information about Emacs.

## Teaching

### Course material

You may be asked for identification to get access to some of the pages below.

### R - information and resources

A collection of resources about R. The selection is somewhat taylored to the courses I teach.

## PhD and MSc projects

I mainly offer projects which fall under the umbrella of `time series analysis`

, ```
statistical
computing
```

, `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
research reports.

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

## Undergraduate projects

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.

## Events

Recent workshops co-organised by me: