Publications of Vlad Stefan BARBU

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Habilitation (HDR)

  • V. S. Barbu. Contributions to Statistical and Probabilistic Topics for semi-Markov and Markov Processes. Habilitation manuscript, University of Rouen - Normandy, 2017

PhD Thesis

  • V. S. Barbu. Estimation des chaînes semi-markoviennes et des chaînes semi-markoviennes cachées en vue d'applications en fiabilité et en biologie (in French), PhD Thesis, University of Technology of Compiègne, France, 2005

Book

  • V. S. Barbu, N. Limnios. Semi-Markov Chains and Hidden Semi-Markov Models toward Applications-Their use in Reliability and DNA Analysis, Lecture Notes in Statistics, vol. 191, Springer, New York 2008. ISBN 978-0-387-73171-1

Referee par Martin Crowder (London Imperial College) : "...Overall, this is a stimulating book. As it says, the discrete-time framework has been underused in the past in view of the fact that much real data in practice (I would even say most) is recorded in discrete time. Also, the applications listed, particularly the DNA sequencing, are important and timely." (International Statistical Review, 77, 2, page 307, 2009)

Edited book

  • V. S. Barbu, N. Vergne (editors), Statistical Topics and Stochastic Models for Dependent Data with Applications, Iste-Wiley, 2020. ISBN: 978-1-786-30603-6

Articles in international peer reviewed journals

  • E. N. Kalligeris, V. S. Barbu, G. Hacques, L. Seifert, N. Vergne, Unveiling the persistent dynamics of visual-motor skills via drifting Markov modeling, Nonlinear Dynamics, Psychology, and Life Sciences, 2024
  • V. S. Barbu, F. Lecocq, C. Lothodé, N. Vergne, smmR: A Semi-Markov R package, Journal of Open Source Software, 8(85), 4365, 2023. DOI: https://doi.org/10.21105/joss.04365
  • V. S. Barbu, G. D’Amico, A. Makrides, A continuous-time semi-Markov system governed by stepwise transitions, Mathematics, 10, 2745, 2022. DOI: https://doi.org/10.3390/math10152745
  • C. Ayhar, V. S. Barbu, F. Mokhtari, S. Rahmani, On the asymptotic properties of some kernel estimators for continuous semi-Markov processes, Journal of Nonparametric Statistics, 34(2), 299-318, 2022. DOI: https://doi.org/10.1080/10485252.2022.2044033
  • T. Gkelsinis, A. Karagrigoriou, V. S. Barbu, Statistical inference based on weighted divergence measures with simulations and applications, Statistical Papers, 63, 1511-1536, 2022. DOI: https://doi.org/10.1007/s00362-022-01286-z
  • V. S. Barbu, S. Beltaief, S. Pergamenshchikov, Adaptive efficient estimation for generalized semi-Markov big data models, Annals of the Institute of Statistical Mathematics, 74, 925-955, 2022. DOI: https://doi.org/10.1007/s10463-022-00820-y
  • L. Hammadi, E. Souza de Cursi, V. S. Barbu, A. A. Ouahman, Risk models-based on uncertainty quantication for illicit traffic time series in customs context, International Journal of Shipping and Transport Logistics, 14(1/2), 3-32, 2022
  • V. S. Barbu, G. D'Amico, T. Gkelsinis , Sequential interval reliability for discrete-time homogeneous semi-Markov repairable systems, Mathematics, 9, 1997, 2021. DOI: https://doi.org/10.3390/math9161997
  • V. S. Barbu, F. Furtunescu, B. Murgescu, C. Pintilescu, Exploring the reliability of research indicators reported by Romanian universities in 2019, Journal of Research in Higher Education, 5(1), 68-94, 2021. DOI: https://doi.org/10.24193/JRHE.2021.1.4
  • V. S. Barbu, A. Karagrigoriou, A. Makrides, Reliability and inference for multi state systems: the generalized Kumaraswamy case, Mathematics, 9, 1834, 2021. DOI: https://doi.org/10.3390/math9161834
  • I. Votsi, G. Gayraud, V. S. Barbu, N. Limnios, Hypotheses testing and posterior concentration rates for semi-Markov processes, Statistical Inference for Stochastic Processes, 24, 707-732, 2021. DOI: https://doi.org/10.1007/s11203-021-09247-3
  • B. Boumaraf, N. Seddik-Ameur, V. S. Barbu, Estimation of Beta-Pareto distribution based on several optimization methods, Mathematics, 8(7), 1055, 2020. DOI: https://doi.org/10.3390/math8071055
  • V. S. Barbu, A. Karagrigoriou, A. Makrides, Statistical inference for a general class of distributions with time-varying parameters, Journal of Applied Statistics, 47(13-15), 2354-2373, 2020. DOI: 10.1080/02664763.2020.1763271
  • V. S. Barbu, S. Beltaief, S. Pergamenshchikov, Robust statistical signal processing in semi-Markov nonparametric regression models, Annales de l'ISUP, 63(2-3), 4555, 2019
  • V. S. Barbu, A. Karagrigoriou, A. Makrides, Estimation and reliability for a special type of semi-Markov process, Journal of Mathematics and Statistics, 2019, 15(1), 259-272, 2019. DOI: 10.3844/jmssp.2019.259.272
  • V. S. Barbu, C. Bérard, D. Cellier, M. Sautreuil, N. Vergne, SMM : An R package for estimation and simulation of discrete-time semi-Markov models, The R Journal, 10(2), 226-247, 2018. DOI: 10.32614/RJ-2018-050
  • V. S. Barbu, N. Vergne, Reliability and survival analysis for drifting Markov models: modelling and estimation, Methodology and Computing in Applied Probability, 21(4), 1407-1429, 2018 (online), 2019 (paperback). DOI: 10.1007/s11009-018-9682-8
  • V. S. Barbu, S. Beltaief, S. Pergamenshchikov, Robust adaptive efficient estimation for a semi-Markov nonparametric regression models, Statistical Inference for Stochastic Processes, 22(2), 187-231, 2018 (online), 2019 (paperback). DOI: https://doi.org/10.1007/s11203-018-9186-8
  • V. S. Barbu, A. Karagrigoriou, V. Preda. Entropy and divergence rates for Markov chains: III. The Cressie and Read case and applications, Proceedings of the Romanian Academy-series A: Mathematics, Physics, Technical Sciences, Information Science, 19(3), 413-421, 2018
  • V. S. Barbu, A. Karagrigoriou, V. Preda. Entropy and divergence rates for Markov chains: II. The weighted case, Proceedings of the Romanian Academy-series A: Mathematics, Physics, Technical Sciences, Information Science, 19(1), 3-10, 2018
  • L. Hammadi, E. Souza de Cursi, V. S. Barbu, A. A. Ouahman, A. Ibourk, A SCOR model for customs supply chain process design, World Customs Journal, 12(2), 95-106, 2018
  • P. A. Ulmeanu, V. S. Barbu, V. Tanasiev, A. Badea. Hidden Markov models revealing the household thermal profiling from smart meter data, Journal of Energy and Buildings, 154, 127-140, 2017. DOI: 10.1016/j.enbuild.2017.08.036
  • V. S. Barbu, A. Karagrigoriou, V. Preda. Entropy and divergence rates for Markov chains: I. The Alpha-Gamma and Beta-Gamma case, Proceedings of the Romanian Academy-series A: Mathematics, Physics, Technical Sciences, Information Science 18(4), 293-301, 2017
  • V. S. Barbu, G. D'Amico, R. De Blasis. Novel advancements in the Markov chain stock model: analysis and inference, Annals of Finance, 13(2), 125-152, 2017. DOI: 10.1007/s10436-017-0297-9
  • V. S. Barbu, G. D'Amico, R. Manca, P. Petroni. Step semi-Markov models and application to manpower management, ESAIM: Probability and Statistics, 20, 555-571, 2016. DOI: 10.1051/ps/2016025
  • V. S. Barbu, A. Karagrigoriou, A. Makrides, Semi-Markov modelling for multi-state systems, Methodology and Computing in Applied Probability, 19(4), 1011-1028, 2016 (online), 2017 (paperback). DOI: https://doi.org/10.1007/s11009-016-9510-y
  • R. Crastes, O. Beaumais, O. Arkoun, D. Laroutis, P.-A. Mahieu, B. Rulleau, S. Hassani-Taibi, V. S. Barbu, D. Gaillard. Erosive runoff events in the European Union: Using discrete choice experiment to assess the benefits of integrated management policies when preferences are heterogeneous, Ecological Economics, 102, 105-112, 2014
  • V. S. Barbu, J. Bulla, A. Maruotti. Estimation of the stationary distribution of a semi-Markov chain, Journal of Reliability and Statistical Studies, 5, 15-26, 2012
  • V. S. Barbu, N. Limnios. Some algebraic methods in semi-Markov processes, In Algebraic Methods in Statistics and Probability, volume 2, series Contemporary Mathematics edited by AMS, eds. Marlos A.G. Viana and Henry P. Wynn, Urbana, 19-35, 2010
  • V. S. Barbu, N. Limnios. Empirical estimation for discrete time semi-Markov processes with applications in reliability, Journal of Nonparametric Statistics, 18(7-8), 483-498, 2006. https://doi.org/10.1080/10485250701261913
  • V. S. Barbu, N. Limnios. Maximum likelihood estimation for hidden semi-Markov models, Comptes rendus de l'Académie des sciences, Paris, Ser. I, 342, 201-205, 2006
  • V. S. Barbu, M. Boussemart, N. Limnios. Discrete time semi-Markov model for reliability and survival analysis, Communications in Statistics-Theory and Methods, 33(11), 2833-2868, 2004

Chapters in peer reviewed books

  • V. S. Barbu, F. Lecocq, N. Vergne, Parametric estimation of censored semi-Markov chains, In Stochastic Modeling and Statistical Methods: Advances and Applications, eds. Alex Karagrigoriou, Sonia Malefaki, Ioannis S. Triantafyllou, Elsevier, 2024
  • L. Seifert, E. N. Kalligeris, J. Komar, G. Hacques, V. S. Barbu, N. Vergne, The application of drifting Markov modelling to dynamics skill acquisition, In Stochastic Modeling and Statistical Methods: Advances and Applications, eds. Alex Karagrigoriou, Sonia Malefaki, Ioannis S. Triantafyllou, Elsevier, 2024
  • V. S. Barbu, A. Karagrigoriou, A. Makrides, Semi-Markov processes for earthquake forecast, In Statistical Methods and Modeling of Seismogenesis, eds. Nikolaos Limnios, Eleftheria Papadimitriou, George Tsaklidis, Wiley-ISTE, 299-308, 2021
  • V. S. Barbu, A. Karagrigoriou, A. Makrides, Modelling and inference for special types of semi-Markov processes, In Stochastic Models in Reliability Engineering, eds. Lirong Cui, Anatoly Lisnianski, Ilia Frenkel, CRC Press/Taylor and Francis, 2020
  • V. S. Barbu, A. Karagrigoriou. Modeling and inference for multi-state systems, In Recent Advances in Multi-State Reliability, eds. Anatoly Lisnianski, Ilia Frenkel, Alex Karagrigoriou, Springer Series in Reliability Engineering, Springer, Berlin, 59-70, 2017.
  • A. P. Ulmeanu, V. S. Barbu, A. Budu. Improving the fault tree analysis with uncertainties for the assessment of power systems dependability, In Statistical, Stochastic and Data Analysis Methods and Applications, eds. Alex Karagrigoriou, Teresa Oliveira and Christos Skiadas, ISAST (International Society for the Advancement of Science and Technology), 63-82, 2015.
  • V. S. Barbu, N. Limnios. Reliability of semi-Markov systems in discrete time: modeling and estimation, Handbook on Performability Engineering, ed. Krishna B. Misra, Springer, 369-380, 2008.
  • V. S. Barbu, N. Limnios. Nonparametric Estimation for failure rate functions of discrete time semi-Markov processes, In Probability, Statistics and Modelling in Public Health, eds. Mikhail Nikulin, Daniel Commenges and Catherine Huber-Carol, Springer, 53-72, 2006.
  • V. S. Barbu, N. Limnios. Discrete time semi-Markov processes for reliability and survival analysis - a nonparametric estimation approach, Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis and Quality of Life, eds. Narayanaswamy Balakrishnan, Mikhail Nikulin, Mounir Mesbah, Nikolaos Limnios, Birkhäuser, Collection Statistics for Industry and Technology, Boston, 487-502, 2004.

Software