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Submitted papers :
- Bruned, V., Mas, A., Wlodarczyk, S. : Weak convergence of Particle Swarm Optimization.
- Emanuel, A., Carluer, J.B., Frangos, S., Pasquino A.V., Brooks, M.D., Pateyron S., Boyer-Clavel, M., Boireau, S.,
Szponarski, W., Rouached, H., Ruffel, S., Mas A., Coruzzi, G.M., Bargmann, B.O., Delannoy, E., Krouk, G. : Combinatorial effects of transcription factors functionally
revealed by single cell TARGET-seq.
Articles :
- Bächtold M., Papet J., Barbe Asensio D., Mas A., Borne S., Ngoua Ondo A. : Predicting performance in exams and deep approach to learning in first year university students: A new look at academic success. To appear in Studies in Higher Education.
- Carré C., Carluer J.B., Chaux C., Estoup-Streiff C., Roche N., Hosy E., Mas A., Kouk G. : NextGen GWAS : Full epistatic interaction maps retrieve part of missing heritability and improve phenotypic prediction, to appear in Genome Biol.
- Breux Y., Mas A., Lapierre L. : On-manifold Probabilistic Iterative Closest Point: Application to underwater karst exploration, The International Journal of Robotics Research. June 2022. [HAL.pdf]
- Bruned V., Cleynen A., Mas A., Wlodarczyk S. : Evaluation of Mineralogy per Geological Layers by Approximate Bayesian Computation. SPE Journal , 25 (05), 2418–2432,(2020). [pdf]
- Carré C., Mas A. : Prediction of Hilbertian autoregressive processes : a Recurrent Neural Network approach, Ann. Isup, 63, 115-128, (2019).
- Carré C., Mas A., Krouk G. : Reverse engineering highlights potential principles of large Gene Regulatory Network design and learning, npj Systems Biology and Applications, Jun 22, 3-17, (2017).
- Brunel, E., Mas. A. and Roche, A. : Non-asymptotic Adaptive Prediction in Functional Linear Models, Journal of Multivariate Analysis , 143, pp. 208-232, (2016). [pdf]
- Mas A., Ruymgaart F. : High Dimensional Principal Projections, Complex Analysis and Operator Theory, 9, 35-63, (2015). [pdf]
- Cezar R., Ienco D., Mas A., Masseglia F., Poncelet P., Pudlo P., Székely E, Teisseire M., Vendrell J.-P., Process for identifying rare events, Patent US 20150363551 A1, WO 2014118343 A2, PCT/EP2014/051963, (2014).
- Hilgert N., Mas A., Verzelen N. : Minimax adaptive tests for the Functional Linear model, Annals of Statistics, 41, n°2, 838-869 (2013).[pdf]
- Crambes C., Mas A. : Asymptotics of prediction in functional linear regression with functional outputs, Bernoulli, 19, No. 5B, 2627-2651, (2013). [pdf]
- Mas A. : Lower bound in regression for functional data by small ball probability representation in Hilbert space, Electronic Journal of Statistics, 6, 1745-1778, (2012).[pdf]
- Biau G., Mas A. : PCA-Kernel estimation, Statistics & Risk Modeling, 29, 19–46 (2012). [pdf]
- Bantignies F., Roure V., Comet Y., Leblanc B., Schuettengruber B.,
Bonnet J., Tixier V., Mas A., Cavalli G. : Polycomb-dependent
regulatory interactions between distant Hox loci in Drosophila,
Cell, 144 (2), 214-226 (2011). [pdf]
- Berlinet A., Elamine A., Mas A. :
Local linear regression for functional data Ann Inst Stat Math, 63, 1047–1075 (2011). [pdf]
- Mas A., Pumo B. : Linear processes for functional data,
The Oxford Handbook of Functional Data, Ferraty and Romain Eds., (2010). [article(ps)]
- Fiorio C., Mas A. : A sharp concentration-based adaptive segmentation algorith, Lecture Notes in Computer Science, 6454, 85-96, (2010).
[pdf]
- Mas A., Pumo B. :
Functional linear regression with derivatives, Journal of
Nonparametric Statistics, 21, 19-40,
(2009). [pdf]
- Mas A.: Local functional principal component analysis, Complex
Analysis and Operator Theory, 2, 135-167,
(2008). [pdf]
- Mas A., Pumo B. :
The ARHD model, Journal of Statistical Planning and Inference,137,
538-553, (2007).[pdf]
- Mas A. :
Testing for the mean of random curves : a penalization
approach, Statistical Inference for Stochastic Processes,
10, n°2, 147-163, (2007). [pdf]
- Mas A. :
Weak convergence in the functional autoregressive model, Journal
of Multivariate Analysis, 98, 1231-1261,
(2007).[pdf]
- Cardot H., Mas A., Sarda P. :
CLT in functional linear regression models, Probability
Theory and Related Fields 138,
325-361,(2007).[pdf]
- Ferraty F., Mas A., Vieu P. :
Advances in nonparametric regression for functional variables, Australian
and New-Zealand Journal of Statistics, 49
(3), 1-20, (2007).[pdf]
- Mas A. :
A sufficient condition for the C.L.T. in the space of nuclear operators
– Applications to covariance of random
functions. Statistics and Probability Letters ,
76, 1503-1509, (2006). [pdf]
- Mas A. : Un nouveau tcl pour les
opérateurs de covariance du modèle ARH(1). Annales de l'Isup,
48,
n°3,49-61,(2004). [pdf]
- Mas A. :
Consistence du prédicteur dans le modèle ARH(1). Annales de
l'Isup, 48,
n°3, 39-48,(2004) .[pdf]
- Mas A., Menneteau L. :
Perturbation approach applied to the asymptotic study of random
operators. Progress in Probability, 55,127-123,
Birkhäuser Verlag, (2003).[pdf]
- Mas A., Menneteau L. :
Principles of moderate deviations for functional autoregressive
process. Journal of Multivariate
Analysis,87,241-260,
(2003). [pdf]
- Mas A. :
Rates of weak convergence for images of measures by families of
mappings. Statistics and
Probability Letters, 56,7-12,
(2002).
- Mas A. : Weak convergence for the
covariance operators of a Hilbertian linear process. Stochastic
Processes and
their Applications, 99,
117-135, (2002). [pdf]
- Cardot H., Ferraty F., Mas A., Sarda P. :
Testing hypotheses in the functional linear model. Scandinavian
Journal of
Statistics, 29,1-15,
(2002). [article
(ps)]
- Mas A. : Normalité asymptotique
de l’estimateur empirique de l’opérateur
d’autocorrélation d’un processus ARH(1). C. R. Acad. Sci.
Paris, 329,
Série I,899-902 (1999).
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