Research interests

Computational statistics; Combinatorial algorithms; Heuristic optimization; Parallel algorithms.

Books

  1. E.J. Kontoghiorghes and C. Gatu, Editors, Optimisation, Econometric and Financial Analysis. Volume 9 of books series Advances in Computational Management Science, Springer, 2006. (url).

Special issues

  1. C. Gatu, J. Gentle, J. Hinde, and M. Huh, guest editors. Special Issue on Statistical Algorithms and Software of Computational Statistics & Data Analysis, 52 (2007), pp. 616-815. (url).
  2. C. Gatu and B. D. McCulloughC, guest editors. 2nd Special Issue on Statistical Algorithms and Software of Computational Statistics & Data Analysis, 54 (2010), pp. 609-802. (url).
  3. P. Filzmoser, C. Gatu and A. Zeileis, guest editors. Special Issue on Statistical Algorithms and Software in R of Computational Statistics & Data Analysis, 71 (2014), pp. 887-1063. (url).
  4. A. Colubi, C. Gatu and E.J. Kontoghiorghes, guest editors. Special Issue on Computational Statistics of International Journal of Computer Mathematics, 93 (2016), pp. 627-722. (url).

Journal papers

  1. C. Gatu and E.J. Kontoghiorghes. Parallel algorithms for computing all possible subset regression models using the QR decomposition. Parallel Computing, 29 (2003), pp. 505-521. (url).
  2. C. Gatu and E.J. Kontoghiorghes. Branch-and-bound algorithms for computing the best subset regression models. Journal of Computational and Graphical Statistics, 15 (2006), pp. 139-156. (url).
  3. C. Gatu and E.J. Kontoghiorghes. Efficient strategies for deriving the subset VAR models. Computational Management Science, 2 (2005), pp. 253-278. (url).
  4. C. Gatu and E.J. Kontoghiorghes. Estimating all possible SUR models with permuted exogenous data matrices derived from a VAR process. Journal of Economic Dynamics and Control, 30 (2006), pp. 721-739. (url).
  5. M. Hofmann, C. Gatu and E.J. Kontoghiorghes. Efficient algorithms for computing the best-subset regression models for large scale problems. Computational Statistics & Data Analysis, 52 (2007), pp. 16-29. (url).
  6. C. Gatu, P. Yanev and E.J. Kontoghiorghes. A graph approach to generate all possible regression submodels. Computational Statistics & Data Analysis, 52 (2007), pp. 799-815. (url).
  7. C. Gatu, E.J. Kontoghiorghes, M. Gilli and P. Winker. An efficient branch-and-bound strategy for Subset Vector Autoregressive model selection. Journal of Economic Dynamics & Control, 32 (2007), pp. 1949-1963. (url).
  8. M. Hofmann, C. Gatu and E.J. Kontoghiorghes. An exact least trimmed squares algorithm for a range of coverage values. Journal of Computational and Graphical Statistics, 19 (2010), pp. 191-204. (url).
  9. C. Gatu and E.J. Kontoghiorghes. A fast algorithm for non-negativity model selection. Statistics and Computing, 23 (2013), pp. 403-411. (url).
  10. M. I. Cosbuc, C. Gatu, A. Colubi and E. J. Kontoghiorghes. A generalized singular value decomposition strategy for estimating the block recursive simultaneous equations model. Computational Economics, 50 (2017), pp. 503-515. (url).
  11. M. Hofmann, C. Gatu, E. J. Kontoghiorghes, A. Colubi and A. Zeileis. lmSubsets: Exact Variable-Subset Selection in Linear Regression for R. Journal of Statistical Software, 93 (2020). (url).

Papers under preparation

  1. C. Gatu, C. Barna, E.J. Kontoghiorghes. A graph approach to find the best cluster-set for each possible number of clusters, 2023.

Book chapters

  1. A. Mandes, C. Gatu, P. Winker. Convergence of Heuristic based Estimators of the GARCH Model. Towards Advanced Data Analysis by Combining Soft Computing and Statistics Eds. C. Borgelt, M.A. Gil, J.M.C. Sousa and M. Verleysen, Springer 2013, pp. 151-163. (url)

Conference papers

  1. C. Gatu, M. Sysi-aho and M. Oresic. A regression subset-selection strategy for fat-structure data. Proceedings of International Conference on Computational Statistics (COMPSTAT 2008), Porto, Portugal, 24-29 August 2008, pp. 349-358. (url)

Software

  1. M. Hofmann, C. Gatu, E. J. Kontoghiorghes, A. Colubi and A. Zeileis. lmSubsets: Exact Variable-Subset Selection in Linear Regression. R package, version 0.5, 2020. (url)

Conference presentations

  1. 7th of September 2001 - IFAC symposium on Modeling and Control of Economic Systems, Klagenfurt, Austria - "A parallel algorithm for computing all possible subset regression models using the QR decomposition".
  2. 14th of February 2002 - ERCIM working group on Matrix Computations and Statistics, second workshop, Rennes, France - "Algorithms for computing the best subset regression models using the QR decomposition".
  3. 28th of June 2002, Computing in Economics and Finance - Eighth international conference of The Society for Computational Economics, Aix en Provence, France - "A branch and bound algorithm for computing the best subset regression models".
  4. 10th of November 2002 - 2nd international workshop on Parallel Matrix Algorithms and Applications, Neuchâtel, Switzerland - "Algorithms for VAR model selection".
  5. 29th of March 2003 - International workshop on Computational Management Science, Economics, Finance and Engineering, Limassol, Cyprus - "Efficient strategies for computing the subset VAR models".
  6. 10th of July 2003 - The 13th international meeting of the Psychometric Society (IMPS2003) and the 68th annual American meeting of the Psychometric Society, Cagliary, Italy - "A branch and bound algorithm for computing the best subset regression models".
  7. 23rd of September 2003 - First international workshop on Numerical Linear Algebra and its Applications, Monopoli (Bari), Italy - "Efficient strategies for deriving the subset VAR models".
  8. 28th of August 2004 - 5th workshop of the ERCIM working group on Matrix Computations and Statistics, Prague, Czech Republic - "A branch-and-bound algorithm for subset Vector Autoregressive model selection".
  9. 19th of October 2004 - 3rd international workshop on Parallel Matrix Algorithms and Applications and workshop on Matrices in Statistics and Optimization, CIRM-Luminy, Marseilles, France - "Algorithms for computing the best-subset regression models".
  10. 30th of October 2005 - International Association for Statistical Computing 3rd World Conference on Computational Statistics and Data Analysis, Limassol, Cyprus - "An efficient branch-and-bound strategy for subset Vector Autoregressive model selection".
  11. 23rd of June 2006 - 12th International Conference on Computing in Economics and Finance of the Society for Computational Economics, Limassol, Cyprus - "A graph approach to generate all possible subset regression models".
  12. 2nd of September 2006 - 8th workshop of the ERCIM working group on Matrix Computations and Statistics, Salerno, Italy - "A regression graph framework for model selection".
  13. 21st of April 2007 - International workshop on Computational and Financial Econometrics, Geneva, Switzerland - "Subset selection of the linear regression model with constraints".
  14. 20th of June 2008 - First Workshop of the ERCIM Working Group on Computing & Statistics, Neuchatel, Switzerland - "Regression subset selection with non-negative coefficients".
  15. 27th of June 2008 - 14th International Conference on Computing in Economics and Finance, Paris, France - "Subset Selection of the Linear Regression Model with Constraints".
  16. 26th of August 2008 - 18th International Conference on Computational Statistics (COMPSTAT 2008), Porto, Portugal - "A regression subset-selection strategy for fat-structure data".
  17. 1st of May 2009 - 6th International Conference on Computational Management Science, Geneva, Switzerland - "A Graph Approach to Find the Best Grouping for Each Possible Number of Clusters".
  18. 26th of October 2009. 2nd International Workshop of the ERCIM Working Group on Computing & Statistics, COMISEF Tutorial on Statistical Model Selection, Limassol, Cyprus - "Computational strategies for subset model selection".
  19. 28th of April 2011. 8th International Conference on Computational Management Science, Neuchatel, Switzerland - "A graph approach to find the best grouping for each possible number of clusters ".
  20. 28th of June 2012 - 7th International Workshop on Parallel Matrix Algorithms and Applications, London, UK - "Directed graphs based strategies for solving the clustering problem".
  21. 1st of December 2012 - 5th International Conference of the ERCIM Working Group on Computing and Statistics, Oviedo, Spain - "Computational strategies for non-negativity model selection".
  22. 21st of August 2014 - 21st International Conference on COMPUTATIONAL STATISTICS (COMPSTAT 2014), Geneva, Switzerland - "A computational efficient strategy for estimating the block recursive simultaneous equations model".
  23. 23rd of August 2014 - Computationally Intensive Methods in Statistics & Econometrics Satellite meeting of COMPSTAT 2014, Neuchatel, Switzerland - "A graph approach for minimum within-cluster sum of squares grouping".
  24. 13th of December 2015 - 8th International Conference of the ERCIM Working Group on Computational and Methodological Statistics (CMStatistics 2015), London, UK - "lmSubsets: An R package for regression subset selection".
  25. 21st of March 2016 - 3rd Working Groups meeting of the CRoNoS COST Action IC1408, Limassol, Cyprus - "Computational strategies for regression model selection".
  26. 26th of August 2016 - 22nd International Conference on COMPUTATIONAL STATISTICS (COMPSTAT 2016), Oviedo, Spain - "Genetic versus controlled approximate algorithms for regression model selection".
  27. 11th of December 2016 - 9th International Conference of the ERCIM Working Group on Computational and Methodological Statistics (CMStatistics 2016), Seville, Spain - "A controlled error approximate algorithm for regression model selection. A comparison with related packages in R".
  28. 18th of December 2017 - 10th International Conference of the ERCIM Working Group on Computational and Methodological Statistics (CMStatistics 2017), London, UK - "Combinatorial strategies for greedy regression selection".
  29. 5th of April 2018 - 1st CRoNoS Workshop on Multivariate Data and Software (CRoNoS MDA 2018) - "Computational strategies for regression model selection" (invited course).
  30. 16th of December 2018 - 11th International Conference of the ERCIM Working Group on Computational and Methodological Statistics (CMStatistics 2018), Pisa, Italy - "A graph approach to find the best grouping for each possible number of clusters".
  31. 15th of April 2019 - 2nd CRoNoS Workshop on Multivariate Data and Software (CRoNoS MDA 2019) - "Computational strategies for regression subset selection".
  32. 27th of May 2019 - Olomoucian Days of Applied Mathematics 2019 (ODAM 2019), Olomouc, Czech Republic - "Computational strategies for subset regression model selection" (invited speaker).
  33. 18th of December 2019 - 12th International Conference of the ERCIM Working Group on Computational and Methodological Statistics (CMStatistics 2019), London, UK - "Enhanced algorithms for hierarchical clustering".
  34. 19th of December 2022 - 15th International Conference of the ERCIM Working Group on Computational and Methodological Statistics (CMStatistics 2022), London, UK - "Computational strategies for regression model selection in the high-dimensional case".
  35. 2nd of August 2023 - 6th International Conference on Econometrics and Statistics (EcoSta 2023), Tokyo, Japan - "Computational strategies for regression model selection in the high-dimensional case". (online)

Invited seminar presentations

  1. 19th of October 2005 - Department of Public and Business Administration, University of Cyprus, Cyprus - "Computational Strategies for Deriving the Best-subset Regression Models".
  2. 13th of June 2006 - Department of Statistics, University of Neuchâtel, Switzerland - "Branch-and-bound strategies for deriving the best-subset regression models".
  3. 21st of December 2006 - Faculty of Computer Science, "Al.I. Cuza" University of Iasi, Romania - "Strategies for deriving the best-subset regression models".
  4. 25th of April 2007 - INRIA-IRISA, Rennes, France - "Computational Strategies for Deriving the Best-subset Regression Models".
  5. 16th of May 2007 - Department of Public and Business Administration, University of Cyprus, Cyprus - "An efficient branch-and-bound strategy for subset Vector Autoregressive model selection".
  6. 22nd of February 2008 - Department of Public and Business Administration, University of Cyprus, Cyprus - "A regression graph framework for statistical model selection".
  7. 17th of June 2011 - Department of Commerce, Finance and Shipping, Cyprus University of Technology, Limassol, Cyprus - "A fast algorithm for non-negativity model selection".
  8. 4th of December 2014 - Department of Statistics, University of Bologna, Italy - "Computational strategies for computing the best subset regression models".