Statistical Learning in Actuarial Applications WP

Aims

Our aim is to construct highly flexible actuarial models such as:

  • finite mixture regression models for the number and the costs of claims;
  • univariate and multivariate regression models with varying dispersion and shape for claim frequencies and severities;
  • copula based models with regression structures on the mean, dispersion and dependence parameters for different claim types and their associated claim counts and costs;
  • dependence modelling in risk management and sensitivity analysis;
  • first-order integer valued autoregressive INAR(1) regression models with varying dispersion for time series of claim counts;
  • neural network embeddings of the aforementioned models which are able to capture the stylized characteristics of structured, semi-structured and unstructured insurance data;
  • Classification of green bonds using statistical learning methods and decarbonization;
  • Gaussian process spatial-temporal regression models; and
  • Heavy tails and Extremes in spatial and temporal settings.

Members

  1. George Tzougas (Chair), Associate Professor, Dept of Actuarial Mathematics and Statistics, Heriot Watt University
  2. Lluís Bermúdez i Morata, Professor Dept de Matemàtica Econòmica, Financera i Actuarial, Universitat de Barcelona
  3. Enrique Calderin, Senior Lecturer, Centre for Actuarial Studies, Unversity of Melbourne
  4. Dimitris Christopoulos, Professor, School of Social Sciences, Edinburgh Business School
  5. Angelos Dassios, Professor, Dept of Statistics, London School of Economics and Political Science
  6. Tsz Chai Fung, Assistant Professor, J. Mack Robinson College of Business, Georgia State University
  7. Emilio Gómez-Déniz, Professor in Dept of Quantitative Methods in Economics and Management, University of Las Palmas de Gran Canaria
  8. Montserrat Guillén, Chair Professor, Dept of Econometrics, University of Barcelona
  9. Himchan Jeong, Assistant Professor of Statistics and Actuarial Science, Simon Fraser University
  10. Dimitris Karlis, Professor, Dept of Statistics, Athens University of Economics and Business
  11. Giampiero Marra, Professor of Statistics, Dept of Statistical Science at University College London
  12. Michael Merz, Professor, Faculty of Business Administration Hamburger Business School, Universität Hamburg
  13. Aristidis Nikoloulopoulos , Associate Professor in Statistics, School of Computing Sciences, University of East Anglia
  14. Gareth Peters, Janet & Ian Duncan Endowed Chair of Actuarial Science, Chair Professor of Statistics for Risk and Insurance, Dept of Statistics & Applied Probability, University of California Santa Barbara
  15. Rosalba Radice, Reader in Statistics, Bayes Business School, City, University of London
  16. José Maria Sarabia, Professor of Statistics and Operations Research and Full Professor of Quantitative Methods, CUNEF Universidad
  17. Dionisios Sotiropoulos, Assistant Professor, Dept of Computer Science, University of Piraeus
  18. George Streftaris, Professor, Dept of Actuarial Mathematics and Statistics, Heriot Watt University
  19. Andreas Tsanakas, Professor, Risk Management, Bayes Business School, City, University of London
  20. Spyridon Vrontos, Senior Lecturer in Actuarial Science, Dept of Mathematical Sciences, University of Essex
  21. Mario V. Wüthrich, Professor for Actuarial Science, Dept of Mathematics at ETH
  22. Xueyuan Wu , Associate Professor, Centre for actuarial studies, Dept of Economics, The University of Melbourne

Research Papers

  1. The multivariate mixed Negative Binomial regression model with an application to insurance a posteriori ratemaking
  2. A first-order binomial-mixed Poisson integer-valued autoregressive model with serially dependent innovations
  3. Bivariate Mixed Poisson Regression Models with Varying Dispersion
  4. EM Estimation for the Bivariate Mixed Exponential Regression Model