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 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.

Proceedings

 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.