College of Engineering, Design and Physical Sciences, Brunel University London

Veronica Vinciotti

Picture of Veronica

Reader in Statistics

Office:  24 Tower A
Telephone No: +44 (0)1895267469
Postal address:   
Department of Mathematics 
Brunel University London


Uxbridge UB8 3PH

Research Interests


Recent and Upcoming Events

Current and Recent PhD Students

Journal Publications
  1. V. Vinciotti and E. Wit (2020) Editorial of special issue on "Statistical Network Science". Statistica Neerlandica, 74, 3, 217-457.
  2. I. Artico, I. Smolyarenko, V. Vinciotti and E. Wit How rare are power-law networks really? To appear in Proceedings of the Royal Society A.
  3. S. Ranciati, V. Vinciotti and E. Wit (2020) Identifying overlapping terrorist cells from the Noordin Top actor-event network. To appear in Annals of Applied Statistics.
  4. L. Augugliaro, G. Sottile and V. Vinciotti (2020) The conditional censored graphical lasso estimator. To appear in Statistics and Computing.
  5. L. Augugliaro, A. Abbruzzo and V. Vinciotti (2020) L1 penalized censored Gaussian graphical model. Biostatistics, 21, 2, e1-e16. R package cglasso
  6. V. Vinciotti, E. Tosetti, F. Moscone and M. Lycett (2019) The effect of interfirm financial transactions on the credit risk of small and medium-sized enterprises. Journal of the Royal Statistical Society, Series A, 182, 4, 1205-1226.
  7. E. Tosetti and V. Vinciotti (2019) A computationally efficient correlated mixed probit model for credit risk inference. Journal of the Royal Statistical Society: Series C, 68, 4, 1183-1204.
  8. A. Peluso, V. Vinciotti and K. Yu (2019) Discrete Weibull generalised additive model: an application to count fertility data. Journal of the Royal Statistical Society: Series C, 68, 3, 565-583.
  9. A. Peluso, P. Berta and V. Vinciotti (2019) Do pay-for-performance incentives lead to a better health outcome? Empirical Economics, 56, 6, 2167-2184.
  10. P. Berta and V. Vinciotti (2019) Multilevel logistic cluster-weighted model for outcome evaluation in healthcare. Statistical Analysis and Data Mining, 12, 434-443.
  11. H. Haselimashhadi, V. Vinciotti and K. Yu (2018) A novel Bayesian regression model for counts with an application to health data. Journal of Applied Statistics, 45, 6, 1085-1105. R package BDWreg
  12. H. Haselimashhadi and V. Vinciotti (2018) Penalised inference for lagged dependent regression in the presence of autocorrelated residuals. METRON, 76, 1, 49-68. R package DREGAR.
  13. H. Klakattawi, V. Vinciotti and K. Yu (2018) A simple and adaptive dispersion model for count data. Entropy, 20, 2, 142. R package DWreg
  14. F. Moscone, V. Vinciotti, E. Tosetti (2018) Large Network Inference: New Insights in Health Economics. Health Econometrics, Volume 294 (Chapter 15), Emerald Publishing Limited.
  15. M. Ferdous, Y. Bao, V. Vinciotti, X. Liu and P. Wilson (2018) Predicting gene expression from genome wide protein binding profiles. Neurocomputing, 275, 1, 1490-1499.
  16. I. de Castro, H. Amin, V. Vinciotti and P. Vagnarelli (2017) Network of phosphatases and HDAC complexes at repressed chromatin. Cell Cycle, 16, 21, 2011-2017.
  17. V. Vinciotti (2017) Modelling ChIP-seq data using HMMs. Methods in Molecular Biology, 1552, 115-122.
  18. V. Vinciotti and E. Wit (2017) Editorial of special issue on "Statistical Network Science and its Applications". Journal of the Royal Statistical Society: Series C, 66, 3, 451-453.
  19. I. de Castro, J. Budzak, M. Di Giacinto, L. Ligammari, E. Gokhan, C. Spanos, D. Moralli, J. de las Heras, E. Schirmer, K. Ullman, W. Bickmore, K. Green, J. Rappsilber, S. Lamble, M. Goldberg, V. Vinciotti and P. Vagnarelli (2017) Repo-Man/PP1 regulates heterochromatin formation in interphase. Nature Communications, 8, 14048.
  20. F. Moscone, E. Tosetti and V. Vinciotti (2017) Sparse estimation of huge networks with a block-wise structure. The Econometrics Journal, 20, 3, S61-S85.
  21. M. Signorelli, V. Vinciotti, E. Wit (2016) NEAT: an efficient network enrichment analysis test. BMC Bioinformatics, 17, 352. R package neat
  22. V. Vinciotti, E. Wit, R. Jansen, E. de Geus, B. Penninx, D. Boomsma and P. 't Hoen (2016) Consistency of biological networks inferred from microarray and sequencing data. BMC Bioinformatics, 17, 254.
  23. V. Vinciotti, L. Augugliaro, A. Abbruzzo and E. Wit (2016) Model selection for factorial Gaussian graphical models with an application to dynamic regulatory networks. Statistical Applications in Genetics and Molecular Biology, 15, 3, 193-212. R package sglasso
  24. H. Hashem, V. Vinciotti, R. Alhamzawi and K. Yu (2016) Quantile regression with group lasso for classification. Advances in Data Analysis and Classification, 10, 3, 375-390.
  25. Z. Wei, R. Hierons, M. Li, X. Liu and V. Vinciotti (2016) Multi-objective optimisation for regression testing. Information Sciences, 334-335, 1-16.
  26. B. Alhaji, H. Dai, Y. Hayashi,V. Vinciotti, A. Harrison and B. Lausen (2016) Bayesian analysis for mixtures of discrete distributions with a non-parametric component. Journal of Applied Statistics, 43, 8, 1369-1385.
  27. Y. Bao, V. Vinciotti, E. Wit and P. 't Hoen (2014) Joint modelling of ChIP-seq data via a Markov random field model. Biostatistics, 15, 2, 296-310. R package enRich
  28. Y. Bao, V. Vinciotti, E. Wit and P. 't Hoen (2013) Accounting for immunoprecipitation efficiencies in the statistical analysis of ChIP-seq data. BMC Bioinformatics, 14:169. R package enRich
  29. V. Vinciotti and H. Hashem (2013) Robust methods for inferring sparse network structures . Computational Statistics and Data Analysis, 67, 84-94.
  30. S. Anvar, A. Tucker, V. Vinciotti, A. Venema, G. van Ommen, S. van der Maarel, V. Raz and P. 't Hoen (2011) Interspecies translation of disease networks increases robustness and predictive accuracy. PLoS Computational Biology, 7, 11, e1002258.
  31. R. Alhamzawi, K. Yu, V. Vinciotti and A. Tucker (2011) Prior elicitation for mixed quantile regression with an allometric model. Environmetrics, 22, 7, 911-920.
  32. C. Harris, P. Hamilton, T. Runnalls, V. Vinciotti, A. Henshaw, D. Hodgson, T. Coe, S. Jobling, C. Tyler and J. Sumpter (2011) The consequences of feminization in breeding groups of wild fish. Environmental Health Perspectives, 119, 3, 306-311.
  33. V. Vinciotti and K. Yu (2009) M-quantile regression analysis of temporal gene expression data. Statistical Applications in Genetics and Molecular Biology, 8, 1, Article 41.
  34. Z. Wang, X. Liu, Y. Liu, J. Liang and V. Vinciotti (2009) An extended Kalman filtering approach to modelling nonlinear dynamic gene regulatory networks via short gene expression time series. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 6, 3, 410-419.
  35. A. Recchia, E. Wit, V. Vinciotti and P. Kellam (2008) Computational inference of replication and transcription activator regulator activity in herpesvirus from gene expression data. IET Systems Biology, 2, 6, 385-396.
  36. R. Khanin and V. Vinciotti (2008) Computational modelling of post-transcriptional regulation by microRNAs. Journal of Computational Biology, 15, 3, 305-316.
  37. R. Khanin, V. Vinciotti, V. Mersinias, C. Smith and E. Wit (2007) Statistical reconstruction of transcription factor activity using Michaelis-Menten kinetics. Biometrics, 63, 816-823.
  38. A. Tucker, P. 't Hoen, V. Vinciotti and X. Liu (2006) Temporal Bayesian classifier for modelling muscular dystrophy expression data. Intelligent Data Analysis, 10, 5, 441-455.
  39. N. Strouthidis, V. Vinciotti, A. Tucker, S. Gardiner, D. Crabb and D. Garway-Heath (2006) Structure and function in glaucoma: the relationship between a functional visual field map and an anatomical retinal map. Investigative Opthalmology and Visual Science, 47, 12, 5356-5362.
  40. R. Khanin, V. Vinciotti and E. Wit (2006) Reconstructing repressor protein levels from expression of gene targets in E.Coli. PNAS, 103, 49, 18592-18596.
  41. V. Vinciotti, X. Liu, R. Turk, E. de Meijer and P. 't Hoen (2006) Exploiting the full power of temporal gene expression profiling through a new statistical test: Application to the analysis of muscular dystrophy data. BMC Bioinformatics, 7, 183.
  42. V. Vinciotti, A. Tucker, P. Kellam and X. Liu (2006) The robust selection of predictive genes via a simple classifier. Applied Bioinformatics, 5, 1, 1-11.
  43. A. Tucker, V. Vinciotti, X. Liu and D. Garway-Heath (2005) A spatio-temporal Bayesian network classifier for understanding visual field deterioration. Artificial Intelligence in Medicine, 34, 2, 163-177.
  44. V. Vinciotti, R. Khanin, D. D'Alimonte, X. Liu, N. Cattini, G. Hotchkiss, G. Bucca, O. de Jesus, J. Rasaiyaah, C. Smith, P. Kellam and E. Wit (2005) An experimental evaluation of a loop versus a reference design for two-channel microarrays. Bioinformatics, 21, 4, 492-501.
  45. S. Swift, A. Tucker, V. Vinciotti, N. Martin, C. Orengo, X. Liu and P. Kellam (2004) Consensus clustering and functional interpretation of gene expression data. Genome Biology, 5, R94.
  46. D. Hand and V. Vinciotti (2003) Local versus global models for classification problems: fitting models where it matters. The American Statistician, 57, 2, 124-131.
  47. D. Hand and V. Vinciotti (2003) Choosing k for two-class nearest neighbour classifiers with unbalanced classes. Pattern Recognition Letters, 24, 1555-1562.
  48. V. Vinciotti and D. Hand (2003) Scorecard construction with unbalanced class sizes. Journal of the Iranian Statistical Society, 2, 2, 189-205.

Selected Talks
Veronica Vinciotti
Last changed on 8/07/2020