| Optimal bonus-malus systems using finite mixture models G Tzougas, S Vrontos, N Frangos ASTIN Bulletin: The Journal of the IAA 44 (2), 417-444, 2014 | 55 | 2014 |
| EM estimation for the Poisson-inverse gamma regression model with varying dispersion: an application to insurance ratemaking G Tzougas Risks 8 (3), 97, 2020 | 22 | 2020 |
| An EM algorithm for fitting a new class of mixed exponential regression models with varying dispersion G Tzougas, D Karlis ASTIN Bulletin: The Journal of the IAA 50 (2), 555-583, 2020 | 21 | 2020 |
| Bonus-malus systems with two-component mixture models arising from different parametric families G Tzougas, S Vrontos, N Frangos North American Actuarial Journal 22 (1), 55-91, 2018 | 20 | 2018 |
| Mixture composite regression models with multi-type feature selection TC Fung, G Tzougas, MV Wüthrich North American Actuarial Journal 27 (2), 396-428, 2023 | 18 | 2023 |
| The negative binomial-inverse Gaussian regression model with an application to insurance ratemaking G Tzougas, WL Hoon, JM Lim European Actuarial Journal 9, 323-344, 2019 | 15 | 2019 |
| Confidence intervals of the premiums of optimal bonus malus systems D Karlis, G Tzougas, N Frangos Scandinavian Actuarial Journal 2018 (2), 129-144, 2018 | 14 | 2018 |
| The design of an optimal bonus-malus system based on the Sichel distribution G Tzougas, N Frangos Modern problems in insurance mathematics, 239-260, 2014 | 14 | 2014 |
| The Multivariate Mixed Negative Binomial Regression Model with an Application to a Posteriori Ratemaking G Tzougas, AP di Cerchiara Insurance: Mathematics and Economics, 2021 | 12 | 2021 |
| Insurance ratemaking using the exponential-lognormal regression model G Tzougas, WH Yik, MW Mustaqeem Annals of Actuarial Science 14 (1), 42-71, 2020 | 10 | 2020 |
| An expectation-maximization algorithm for the exponential-generalized inverse gaussian regression model with varying dispersion and shape for modelling the aggregate claim amount G Tzougas, H Jeong Risks 9 (1), 19, 2021 | 8 | 2021 |
| Enhancing logistic regression using neural networks for classification in actuarial learning G Tzougas, K Kutzkov Algorithms 16 (2), 99, 2023 | 6 | 2023 |
| Multivariate claim count regression model with varying dispersion and dependence parameters H Jeong, G Tzougas, TC Fung Journal of the Royal Statistical Society Series A: Statistics in Society 186 …, 2023 | 6 | 2023 |
| Bivariate Mixed Poisson Regression Models with Varying Dispersion G Tzougas, AP di Cerchiara North American Actuarial Journal, 2021 | 6 | 2021 |
| Risk classification for claim counts and losses using regression models for location, scale and shape G Tzougas, SD Vrontos, NE Frangos Variance 9 (1), 140-157, 2015 | 6 | 2015 |
| The multivariate Poisson‐Generalized Inverse Gaussian claim count regression model with varying dispersion and shape parameters G Tzougas, D Makariou Risk Management and Insurance Review 25 (4), 401-417, 2022 | 3 | 2022 |
| The role and significance of green bonds in funding transition to a low carbon economy: A case study forecasting portfolios of green bond instrument returns G Peters, R Zhu, G Tzougas, G Rabitti, I Yusuf Available at SSRN 4299196, 2022 | 2 | 2022 |
| A combined neural network approach for the prediction of admission rates related to respiratory diseases A Jose, AS Macdonald, G Tzougas, G Streftaris Risks 10 (11), 217, 2022 | 2 | 2022 |
| A finite mixture modelling perspective for combining experts’ opinions with an application to quantile-based risk measures D Makariou, P Barrieu, G Tzougas Risks 9 (6), 115, 2021 | 2 | 2021 |
| Neural Network Embedding of the Mixed Poisson Regression Model for Claim Counts. 2021 G Tzougas, Z Li | 2 | |
Spyridon VrontosProfessor of Actuarial Science, Department of Mathematical Sciences, University of EssexVerified email at essex.ac.uk
