My main topics of interest are:

Multivariate time series analysis;
Non and semi parametric models;
Estimation of dependance structures;
Wavelets and applications;
Multiple testing;
Generalized regressions;
Classification of functional data.

My application field deals among others with neurosciences: functional MRI, EEG and MEG recordings.


Articles in journals with reviewing committee

S. Achard, I. Gannaz (2019). Wavelet-based multivariate Whittle estimation, comparison with Fourier : multiwave. Journal of Statistical Software, Vol 69, N°6, pp 1-31. <link> [Software].
S. Achard, I. Gannaz (2016). Multivariate wavelet Whittle estimation in long-range dependence. Journal of Time Series Analysis, Vol 37, pp 476-512. <link> (prebublication).
I. Gannaz (2013). Wavelet penalized likelihood estimation in generalized functional models. TEST, Vol 22, N°1, pp 122-158. <link> (prebublication).
I. Gannaz, O. Wintenberger (2010). Adaptive density estimation under weak dependence. ESAIM: Probability and Statistics, Vol 14, pp 151-172. <link> (prebublication).
I. Gannaz (2007). Robust estimation and wavelet thresholding in partially linear models. Statistics and Computing, Vol 17, N° 4, pp 293-310. <link> (prepublication).

Conference proceedings with reviewing committee

I. Gannaz, S. Achard, M. Clausel, F. Roueff (2017). Analytic wavelets for multivariate time series analysis. In Wavelets and Sparsity XVII (Vol. 10394, p. 103941X). International Society for Optics and Photonics. <link>
I. Gannaz, S. Achard, M. Clausel, F. Roueff (2017). Ondelettes analytiques, application à l’analyse des processus multivariés à longue mémoire. In GRETSI 2017.<link> (in french)
F. Millioz, I. Gannaz (2017). Estimation des paramètres d’un bruit gaussien généralisé basée sur le kurtosis des statistiques minimales. In GRETSI 2017. <link> (in french)
I. Gannaz (2014). Classification of EEG recordings in auditory brain activity via a logistic functional linear regression model. Contributions in infinite-dimensional statistics and related topics, International Workshop on Functional and Operatorial Statistics. <link> <pdf>
I. Gannaz (2012). Estimation par ondelettes dans des modèles fonctionnels généralisés. 44èmes Journées de Statistique de la SFDS. (in french)

Book chapters

S. Achard, I. Gannaz (2018) Wavelet Whittle Estimation in Multivariate Time Series Models: Application to fMRI Data. In: Bertail P., Blanke D., Cornillon PA., Matzner-Løber E. (eds) Nonparametric Statistics. ISNPS 2016. Springer Proceedings in Mathematics & Statistics, vol 250. Springer, Cham


Estimation par ondelettes dans les modèles partiellement linéaires. 2007, thèse de l'université Joseph Fourier, Grenoble, sous la direction d'Anestis Antoniadis. (in french) <pdf>


S. Achard and I. Gannaz (2016), R-package multiwave.
Matlab code.
Authors: S. Achard and I. Gannaz. We are grateful to G. Fay, E. Moulines, F. Roueff, M.S. Taqqu and Shimotsu for kindly providing their codes. The functions used for wavelet decomposition were implemented by G. Fay, E. Moulines, F. Roueff and M.S. Taqqu.
Associated publication: S. Achard, I. Gannaz, Multivariate wavelet Whittle estimation in long-range dependence. 2016, Journal of Time Series Analysis, Vol 37, pp 476-512.
I. Gannaz and M. Roux (2019), R-package TestCor.
Associated publication: Roux, M. (2018). Graph inference by multiple testing with application to Neuroimaging, Ph.D., Université Grenoble Alpes, France. <link>.
Dernière mise à jour : 14/06/2019
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