dimanche 14 mars 2010


Teaching books

- Collectif Paul Toulouse. "Thèmes de probabilités et statistiques". Collection Agrégation Mathématiques. Masson (Dunod), 1999.

- Azaïs J.M. et Bardet, J.M."Le modèle linéaire par l’exemple", Deuxième Edition, Dunod, 2012.

Published research papers

Published articles

- Bardet J.M. ; Lang, G. ; Moulines, E. and Soulier, P. (2000) Wavelet estimator of long-range dependent processes. Statistical Inference for Stochastic Processes, 3, p. 85-99.

- Bardet, J.M. (2000). Testing for the presence of self-similarity of Gaussian time series having stationary increments. J. of Time Series Anal., 21, p. 497-516.

- Bardet, J.M. (2002). Statistical study of the wavelet analysis of fractional Brownian motion. IEEE Trans. Inform. Theory. 48, p. 991-999.

- Bardet, J.M. (2002) Bivariate occupation measure dimension of multidimensional processes. Stochastic Process. Appl., 99, p. 323-348.

- Azaïs, J.M., Bardet, J.M. and Wschebor, M. (2002). On the tails of the distribution of the maximum of a smooth stationary Gaussian process. ESAIM Prob. Stat., 6, p. 177-185.

- Bardet, J.M. and Bertrand, P. (2007). Definition, properties and wavelet analysis of the multiscale fractional Brownian motion. Fractals, 15, 73-87.

- Bardet, J.M. and Bertrand, P. (2007). Identification of the multiscale fractional Brownian motion with biomechanical applications. J. of Time Series Anal., 28, 1-52.

- Bardet, J.M. and Kammoun, I. (2008). Asymptotic properties of the D.F.A. method. IEEE Trans. Infor. Theory., 54, 2041-2052

- Bardet, J.M., Bibi, H. and Jouini, A. (2008). Adaptive Wavelet based estimators for long range stationary Gaussian processes. Bernoulli, 14, 691-724.

- Bardet, J.M., Doukhan, P. and Leon, J.R. (2008). Uniform limit theorems for the periodogram of weakly dependent time seriesand their applications to Whittle’s estimate. J. of Time Series Anal., 29, 906-945.

- Bardet, J.M., Doukhan, P. and Leon, J.R. (2008). A functional limit theorem for $eta$-weakly dependent processes and its applications. Statistical Inference for Stochastic Processes, 11, 3, 265-280.

- Bardet, J.M., Doukhan, P., Lang G. and Ragache, N. (2008). The standard Lindeberg method applied to weakly dependent processes. ESAIM Prob. Stat., 12, 154-172.

- Bardet, J.-M. and Wintenberger, O. (2009). Asymptotic normality of the Quasi-Maximum Likelihood Estimator for multidimensional causal processes. Ann. Statist., 37, 2730-2759.

- Bardet, J.M., Billat, V. and Kammoun, I. (2009). Modélisation des fréquences cardiaques instantanées durant un marathon et estimation de leurs paramètres fractals. J. Soc. Fr. Stat. & Rev. Stat. Appl., 150, p. 101-126.

- Bardet, J.M. and Bertrand, P. (2010). A nonparametric estimator of the spectral density of a continuous-time Gaussian Process observed at random times. Scand. J. Stat., 38, p. 458-476.

- Bardet, J.-M. and Tudor, C. (2010). A wavelet analysis of the Rosenblatt process : chaos expansion and estimation of the self-similarity parameter Stochastic Processes and Applications, 120, p. 2331-2362.

- Bardet, J.-M. and Surgailis, D. (2011). Measuring the roughness of random paths by increment ratios. Bernoulli, 17, 749-780.

- Bardet, J.M. and Bibi, H. (2012). Adaptive semiparametric wavelet estimator and goodness-of-fit test for long memory linear processes. Electronic Journal of Statistics, 6, 2383-2419

- Bardet, J.M., Billat, V. and Kammoun, I. (2012). A new process to model heartbeat signal during exhaustive run and an adaptive estimator of its fractal parameters. Journal of Applied Statistics, 39, 1331-1351.

- Bardet, J.M. and Dola, B. (2012).Adaptive estimator of the memory parameter and goodness-of-fit test using a multidimensional increment ratio statistic. Journal of Multivariate Analysis, 105, p. 222-240.

- Bardet, J.-M., Kengne, W. and Wintenberger, O. (2012). Detecting multiple change-points in general causal time series using penalized quasi-likelihood. Electronic Journal of Statistics, 6, 435-477.

- Bardet, J.-M. and Surgailis, D. (2013). Nonparametric estimation of the local Hurst function of multifractional processes. Stochastic Processes and Applications, 123, p. 1004-1045.

- Bardet, J.-M. and Surgailis, D. (2013). Moment bounds and central limit theorems for Gaussian subordinated arrays. Journal of Multivariate Analysis, 114, 456-473.

- Bardet, J.-M. and Kengne, W. (2014). Monitoring procedure for parameter change in causal time series. Journal of Multivariate Analysis, 125, 204-221.

- Bardet, J.M. and Tudor, C. (2014). Asymptotic behavior of the Whittle estimator for the increments of a Rosenblatt process. Journal of Multivariate Analysis, 131, 1-16.

- Thommeret, N., Bailly, J.-S., Bardet, J.-M., Kaiser, B. et Puech, C. (2014). Dimensions fractales de réseaux vectoriels : méthodes d’estimation et robustesse des résultats. Cybergeo.

- Bardet, J.M. and Dola, B. (2016). Semiparametric Stationarity and Fractional Unit Roots Tests Based on Data-Driven Multidimensional Increment Ratio Statistics. Journal of Time Series Econometrics, 8, 115-153.

- Bardet, J.M., Boularouk, Y. and Djaballah, K. (2017). Asymptotic behavior of the Laplacian quasi-maximum likelihood estimator of affine causal processes. Electronic journal of statistics, 11, 452-479.

- Bardet, J.M. and Dimby, F. (2017). A new non-parametric detector of univariate outliers for distributions with unbounded support. Extremes, 20, 751-775.

- Bardet, J.M., Fokianos, K. and Neumann, M. (2017). Editorial for the special issue in honour of Paul Doukhan, Statistics, 51, pp.1-2.

- Bardet, J.M. and Dion, C. (2018). Robust semi-parametric multiple change-point detection. Signal Processing, 156, 145-155.

- Bardet, J.M. and Doukhan, P. (2018). Non-parametric estimation of time varying AR(1)–processes with local stationarity and periodicity. Electronic Journal of Statistics, 12, 2323 - 2354.

- Bardet, J.M. Kare K. and Kengne, W. (2020). Consistent model
selection criteria and goodness-of-fit test for common time series models.

Electronic Journal of Statistics, 14, 2009-2052.

- Bardet, J.M. and Guenaizi, A. (2020). Data-driven semi-parametric detection of multiple changes in long-range dependent processes, Electronic Journal of Statistics, 14, 3606-3043.

- Dhifaoui, Z. and Bardet, J.-M. (2021). Local correlation dimension of multidimensional stochastic process. Statistics & Probability Letters.

- Bardet, J.-M., Doukhan, P. and Wintenberger, O. (2022). Contrast estimation of time-varying infinite memory processes.. Stochastic Processes and their Applications.

- Boularouk, Y. and Bardet, J.-M. (2022). Generalized Gaussian quasi-maximum likelihood estimation for most common time series. Communications in Statistics – Theory and Methods.

- Bardet, J.-M. Kare K. and Kengne, W. (2022). Efficient and consistent data-driven model selection for time series. Bernoulli.

- Bardet, J.-M. (2022). Laplace’s method and BIC model selection for least absolute value criterion. Statistic and Probability Letters.

- Bardet, J.-M. (2023). A new estimator for LARCH processes. Journal of Times Series Analysis.

Chapters of books

- Bardet, J.M. ; Lang, G. ; Oppenheim, G. ; Philippe, A. Stoev, S. and Taqqu, M. (2003). Semi-parametric estimation of the long-range dependent processes : A survey. Long-range Dependence : Theory and Applications, Birkhauser.

- Bardet, J.M. ; Lang, G. ; Oppenheim, G. ; Philippe, A. and Taqqu, M. (2003). Generators of long-range dependent processes : A survey. Long-range Dependence : Theory and Applications, Birkhauser.

- Bardet, J.M. (2018). Theoretical and numerical comparisons of the parameter estimator of the fractional Brownian motion. Mathematical Structures and Applications (In Honor of Mahouton Norbert Hounkonnou), 153-173, Springer.

Journal with national board

- Bardet J.M. (1998). Dimension de corrélation locale et dimension de Hausdorff des processus vectoriels continus. C. R. Acad. Sci. Paris Sér. I Math. 326, p. 589-594.

- Bardet J.M. (1999). Un test d’auto-similarité pour les processus gaussiens à accroissements stationnaires. C. R. Acad. Sci. Paris Sér. I Math., 328, p. 521-526.

- Bardet, J.M. (1999). La mémoire longue en économie : discussion. Journal de la SFDS, 140, p. 49-54.

- Bardet, J.M. (2000). Les cours d’actifs financiers sont-ils autosimilaires ? Journal de la SFDS, 141, p. 137-148.

- Bardet, J.M. and Kammoun, I. (2008). Detecting abrupt changes of the long-range dependence or the self-similarity of a Gaussian process
C. R. Math. Acad. Sci. Paris, 346, 889-894.

- Bardet, J.M., Bertrand, P. et Billat, V. (2008). Estimation non-paramétrique de la densité spectrale d’un processus gaussien échantillonné aléatoirement.
Ann. I.S.U.P., 52, 123-138.


- Bardet, J.M. ; Moulines, E. and Soulier, P. (1998). Recent advances on the semi-parametric estimation of the long-range dependence coefficient. ESAIM Proc., 5, Soc. Math. Appl. Indust., Paris, p. 29-41.

- Bertrand, P. ; Bardet, J.M. ; Dabonneville, M. and Mouzat, A. (2001) Automatic Determination of the Different Control Mechanisms in Upright Position by a Wavelet Method. IEEE Engineering in Medicine and Biology Society, 25 - 28, Istambul.

- Bardet, J.M., Faure, C., Lacaille, J. and Olteanu, M. (2016).Comparison of three algorithms for parametric change-point detection. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2016), Bruges, Belgique.

- Bardet, J.M., Faure, C., Lacaille, J. and Olteanu, M. (2017). Unequal time series clustering applied on flight data. Proceedings of the 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM+), Nancy, France.

- Bardet, J.M., Brault, V., Dachian, S., Enikeeva, F. and Saussereau, B. (2020). Change-point detection, segmentation, and related topics, ESAIM : Proceedings and Surveys, 68, 97-122.


- Bardet, J.M. and Kammoun, I. (2007). Detecting changes in the fluctuations of a Gaussian process and an application to heartbeat time series. Preprint.