Mehmet Cadirci
Research student,
- cadircims@cardiff.ac.uk
- 07405 681438
- Room M/1.10, 21-23 Senghennydd Road, Cathays, Cardiff, CF24 4AG
Research
Research interests
Education
M.Sc Mathematics, (University of Sussex, 2016-2017)
Research interests
- Statistics
- Probability Theory
- Shannon-Renyi Entropy
- Kullback Leibler Divergence
- Statistical Inference
Teaching
- Maths Support
- Foundations of Probability (Tutorials)
Thesis
Entropy based on the goodness-of-fit tests for multivariate distributions
This thesis offers a non-parametric test of goodness-of-fit for a class of multivariate generalized Gaussian distributions based on the maximum Shannon entropy principle andthe k-th nearest neighbour distances method of Shannon entropy. It also presents a non parametric test of goodness-of-fit for a classes of multivariate Student and Pearson type II (or Barenblatt ) distributions based on the maximum Rényi entropy principle. Moreover, it introduces three generalizations of von Mises-Fisher distribution on a sphere
and the maximum Shannon entropy principle for them. Based on the L^2-consistency of the k-th nearest neighbour estimate of Shannon entropy. The results are supported by a Monte Carlo simulation.
Supervisors
Publications
2022
- Cadirci, M. S., Evans, D., Leonenko, N. and Makogin, V. 2022. Entropy-based test for generalized Gaussian distributions. Computational Statistics & Data Analysis 173, article number: 107502. (10.1016/j.csda.2022.107502)
2021
- Leonenko, N., Makogin, V. and Cadirci, M. 2021. The entropy based goodness of fit tests for generalized von Mises-Fisher distributions and beyond. Electronic Journal of Statistics 15(2), pp. 6344-6381. (10.1214/21-ejs1946)
- Cadirci, M. 2021. Entropy-based goodness-of-fit tests
for multivariate distributions. PhD Thesis, Cardiff University.