1. S. Nakakita, P. Alquier, M. Imaizumi (2022). Dimension-free Bounds for Sum of Dependent Matrices and Operators with Heavy-Tailed Distribution. Arxiv-2210.09756.
  2. A. Leucht, M. H. Neumann (2023) A log-linear model for non-stationary time series of counts Arxiv-2307.01315.
  3. X. Milhaud, D. Pommeret, Y. Salhi, P. Vandekerkhove (2023). Contamination source based K-sample clustering. Hal-04129130.
  4. K. Barigou, N. El Karoui, S. Loisel, Y. Salhi (2023) Surveillance of actuarial assumptions in the ERM framework. Preprint.
  5. X. Fan, Q.-M. Shao (2024) Self-normalized Cramér type moderate deviations for martingales and applications. Submitted to Bernoulli. Arxiv-2309.05266.
  6. C. Aaron, P. Doukhan, L. Reboul (2024) Geometry under dependence. Submitted to JMVA. Hal-04375650v2.
  7. A. Alcaraz, G. Durrieu, A. Poterie (2024) Quantile regression for longitudinal datal with controlled within-individual variance. Submitted to Statistics.

Preprints may be found on Arxiv and Hal.

  1. P. Doukhan, F. Roueff, J. Rynkiewicz (2020). Spectral estimation for non-linear long range dependent discrete time trawls. Electronic Journal of Statistics, 14, 3157–3191. ISSN: 1935-7524. Doi 20-EJS1742.
  2. P. Alquier, K. Bertin, P. Doukhan, R. Garnier (2020). High-dimensional VAR with low-rank transition. Statistics and Computing 30, 1139–1153. Doi s11222-020-09929-7.
  3. A. Fernández-Fontelo, D. Moriña, A. Cabaña, A. Arratia, P. Puig (2020). Estimating the real burden of disease under a pandemic situation: The SARS-CoV2 case. PLoS ONE 15(12): e0242956. Doi journal.pone.0242956. Arxiv-2008.00262.
  4. N. Mamode Khan, H. Bakouch, A. D. Soobhug, M. G. Scotto (2020). Insights on the trend of the Novel Coronavirus 2019 series in some Small Island Developing States: A Thinning-based Modelling Approach. Alexandria Engineering Journal. Pdf file. Doi j.aej.2020.12.047.
  5. W. Kengne (2020). Strongly consistent model selection for general causal time series. Statistics. Doi j.spl.2020.109000.
  6. T. Huillet (2020). Statistics of Branched Populations Split into Different Types. Applications and Applied Mathematics, 15:2, 764–800. ISSN: 1932-466.
  7. P. Doukhan, N. Mamode Khan, M. H. Neumann (2021). Mixing properties of integer-valued Skellam GARCH processes. ALEA, Lat. Am. J. Probab. Math. Stat. 18, 401–420. Doi 10.30757/ALEA.v18-18.
  8. B. Goncalves, T. Huillet (2021). A generating function approach to Markov chains undergoing binomial catastrophes. Journal of Statistical Mechanics: Theory and Experiments 033402 Doi 1742-5468/abdfcb., Arxiv-2101.03851.
  9. P. Doukhan, K. Fokianos, J. Rynkiewicz (2021). Mixtures of nonlinear Poisson autoregressions. Doi 10.1111/jtsa.12558. J. Time Ser. Anal. 42: 107–135. Doi 20-EJS1742.
  10. Z. M. Debaly, L. Truquet (2021). Iterations of dependent random maps and exogeneity in nonlinear dynamics. Econometric Theory, 37:6, 1135-1172. Arxiv-1908.00845. Doi 10.1017/S0266466620000559.
  11. T. Huillet, S. Martinez (2021). Revisiting John Lamperti's maximal branching process, Stochastics, 94:2, 277-310 Doi 10.1080/17442508.2021.1935949. Arxiv version.
  12. G. Pang, D. Alemayehu, V. de la Peña, M. J. Klass (2021). On the bias and variance of odds ratio, relative risk and false discovery proportion, Communications in Statistics - Theory and Methods. Doi 03610926.2020.1867744.
  13. B. Goncalves, T. Huillet (2021). Keeping random walks safe from extinction and overpopulation in the presence of life-taking disasters. Mathematical Population Studies. Doi 08898480.2021.1976476.
  14. X. Milhaud, D. Pommeret, Y. Salhi, P. Vandekerkhove (2021). R-Project. admix: Package Admix for Admixture Models. Shape constraint free two-sample contamination model testing: explanatory document. Hal-03201760.
  15. B. Goncalves, T. Huillet, E. Löcherbach (2022). On population growth with catastrophes. Stochastic Models, 38:2. Doi 10.1080/15326349.2021.2020660. Arxiv-2007.03277.
  16. R. Garnier (2022). Concurrent neural network: a model of competition between times series. Annals of Operation Research, 313, 945–964. Doi 10.1007/s10479-021-04253-3. Arxiv-2009.14610.
  17. P. Doukhan, A. Leucht, M. H. Neumann (2022). Mixing properties of non-stationary INGARCH(1,1) processes. Bernoulli 28:1, 663-688. Doi 10.3150/21-BEJ1362. Arxiv-2011.05854v2.
  18. P. Doukhan, J. Rynkiewicz, Y. Sahli (2022). Optimal Neighborhoods Selection for AR-ARCH Random Fields with Application to Mortality. Stats., Special Issue "Modern Time Series Analysis".
  19. B. Bobbia, P. Doukhan, X. Fan (2022). Selected topics on weak dependence condition. Graduate J. Math. 7:2, 76-94. Hal-03325994.
  20. M. L. Diop, W. Kengne (2022). Inference and model selection in general causal time series with exogenous covariates. Electronic Journal of Statistics 16:1, 116-157.
  21. F. Maddanu, T. Proietti (2022) Modelling Persistent Cycles in Solar Activity. Solar Physics, 297:13. Doi 11207-021-01943.
  22. R. Garnier, R. Langhendries (2022). Concentration inequalities for non-causal random fields. Electronic Journal of Statistics 16:1, 1681-1725. Doi 10.1214/22-EJS1992.
  23. T. Huillet (2022). Chance Mechanisms Involving SIBUYA Distributions and its relatives. Sankhya B, 84, 722–764.
  24. K. Barigou, P.-O. Goffard, S. Loisel, Y. Salhi (2022). Bayesian model averaging for mortality forecasting using leave-future-out validation. Arxiv 2103.15434, available online 18 March, International Journal of Forecasting,
  25. Y. Jiao, Y. Salhi, S. Wang (2022). Dynamic Bivariate Mortality Modelling. Methodology and Computing in Applied Probability. Doi 10.1007/s11009-022-09955-0.
  26. T. Proietti, F., Maddanu (2022). Modelling cycles in climate series: The fractional sinusoidal waveform process. Doi j.jeconom.2022.04.008, Journal of Econometrics, ISSN 0304-4076.
  27. F. Maddanu (2022). Forecasting highly persistent time series with bounded spectrum processes. Statistical Papers. DOI s00362-022-01321-z
  28. P. Alquier, N. Marie, A. Rosier (2022). Tight risk bound for high dimensional time series completion. Electron. J. Statist. 16:1, 3001-3035. Doi 10.1214/22-EJS2015.
  29. V. de la Pena, P. Doukhan, Y. Sahli (2022). A Dynamic Taylor's Law. Journal of Applied Probability, 59:2 , 584-607, Doi 10.1017/jpr.2021.40.
  30. J. M. Bardet, P. Doukhan, O. Wintenberger (2022). Contrast estimation of time-varying infinite memory processes. Arxiv-2005.07397, Stochastic Processes and their Applications, Doi
  31. J. Cohen, T. Huillet (2022). Taylor's Law for some infinitely divisible probability distributions from population models. Journal of Statistical Physics 188:33. Doi 10.1007/s1095.
  32. X. Fan, P. Alquier, P. Doukhan (2022). Deviation inequalities for stochastic approximation by averaging. Stochastic Processes and their applications. Doi
  33. V. Czellar, D. T. Frazier, E. Renault (2022). Approximate Maximum Likelihood for Complex Structural Models. Journal of Econometrics, 231, 432–456. Doi .jeconom.2021.05.009.
  34. H. Cherrat, J.-L. Prigent (2022). On the Hedging of Interest Rate Margins on Bank Demand Deposits. Computational Economics. Doi 10.1007/s10614-022-10287-x.
  35. P. Doukhan, X. Fan, Z.-Q. Gao (2023). Cramér moderate deviations for a supercritical Galton-Watson process. Statistics & Probability Letters, 192. Doi j.spl.2022.109711. Arxiv-2109.12374.
  36. W. Kengne (2023). On consistency for time series model selection Stat Inference Stoch Process, December 2022. Doi s11203-022-09284-6.
  37. B. Goncalves, T. Huillet, E. Löcherbach (2023). On decay-surge population models. Advances in Applied Probability, 55:2, 444-472. Doi 10.1017/apr.2022.30.
  38. P. Doukhan, M. H. Neumann, L. Truquet (2023). Stationarity and ergodic properties for some observation-driven models in random environments. Ann. Appl. Probab. 33:6B, 5145-5170. Arxiv-2007.07623. Doi 10.1214/23-AAP1944
  39. R. Garnier, R. Langhendries, J. Rynkiewicz (2023). Hold-out estimates of prediction models for Markov processes. Statistics, 57:2. Doi 10.1080/02331888.2023.2183203.
  40. F. Maddanu, T. Proietti (2023). Trends in atmospheric ethane. Climatic Change 176. Doi 10.1007/s10584-023-03508-1.
  41. M. L. Diop, W. Kengne (2023). A general procedure for change-point detection in multivariate time series. Test. . Arxiv-2103.13336.
  42. X. Milhaud, D. Pommeret, Y. Salhi, P. Vandekerkhove (2023). Shape constraint free two-sample contamination model testing. Bernouilli. Hal-03201760.
  43. T. Huillet, M. Möhle (2023). On Bernoulli trials with unequal harmonic success probabilities. Metrika. Arxiv-2211.17044.
  44. R. J. Sternberg, C. H. Wong, B. Baydil (2023). Understanding and Assessing Scientific Wisdom. Accepted for publication in Roeper Review. Doi to come.
  45. D. Moriña, A. Fernández-Fontelo, A. Cabaña et al. (2023). Estimated Covid-19 burden in Spain: ARCH underreported non-stationary time series. BMC Med Res Methodol 23, 75. Doi 10.1186/s12874-023-01894-9.
  46. B. Alimoradian, J. Jakubiak, S. Loisel, Y. Salhi (2023). Understanding Key Drivers of Participant Cash Flows for Individually Managed Stable Value Funds Risks 11:8, 148-175. Doi 10.3390/risks11080148.
  47. B. Alimoradian, J. Jakubiak, S. Loisel, Y. Salhi (2023). A Quantitative Risk Management Framework for Stable Value Fund Wraps. SSRN 4255325, 2022.
  48. H. Wu, X. Fan, Z. Gao, Y., YE (2023). Wasserstein-1 Distance and Nonuniform Berry-Esseen Bound for a Supercritical Branching Process in a Random Environment. Journal of Mathematical Research with Applications. 43:6, 737-753. Doi 10.3770/j.issn:2095-2651.2023.06.009.
  49. T. Huillet (2024). Occupancy Problems Related to the Generalized Stirling Numbers Journal of Statistical Physics, 191:5. Doi 10.1007/s10955-023-03216-1.
  50. P. Alquier (2024). User-friendly introduction to PAC-Bayes bounds. Foundations and Trends in Machine Learning. 17:2, 174-303.
  51. V. de la Peña, H. Gzyl, S.Mayoral, H. Zou, D. Alemayehu (2024). Prediction and estimation of random variables with infinite mean or variance Comm. in Stat.- Theory and Method. Doi 10.1080/03610926.2024.2303976.
  52. R. J. Sternberg, A. S. Dashtaki, B. Baydil (2024). An Empirical Test of the Concept of the Adaptively Intelligent Attitude. Journal of Intelligence, Vol. 12, Number 5, pp. 49.
  53. G. Kermarrec, F. Maddanu, A. Klos, T. Proietti, J. Bogusz (2024) Modelling Trends and Periodic Components. Journal of Geodesy 98, 17.

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In order to be included in the current page, please mention:
This work was funded by CY Initiative of Excellence
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