Codes MSC :
62J05 - Linear regression
62J12 - Generalized linear models
62P10 - Applications of statistics to biology and medical sciences

Ressources complémentaires :
https://www.cirm-math.fr/RepOrga/2146/Slides/joseph_salmon.pdf

Voir aussi

• [Virtualconference] Experimenting in equilibrium / Auteur de la Conférence Wager, Stefan. CIRM
  
• [Virtualconference] Structure learning for CTBN's / Auteur de la Conférence Miasojedow, Błażej. CIRM
  
• [Virtualconference] The price of competition: effect size heterogeneity matters in high dimensions! / Auteur de la Conférence Wang, Hua. CIRM
  
• [Virtualconference] Scaling of scoring rules / Auteur de la Conférence Wallin, Jonas. CIRM
  
• [Virtualconference] Hierarchical bayes modeling for large-scale inference / Auteur de la Conférence Yekutieli, Daniel. CIRM
  
• [Virtualconference] Change: detection, estimation, segmentation / Auteur de la Conférence Siegmund, David. CIRM
  
• [Virtualconference] High-dimensional, multiscale online changepoint detection / Auteur de la Conférence Samworth, Richard. CIRM
  
• [Virtualconference] Knockoff genotypes: value in counterfeit / Auteur de la Conférence Sabatti, Chiara. CIRM
  
• [Virtualconference] Optimal and maximin procedures for multiple testing problems / Auteur de la Conférence Rosset, Saharon. CIRM
  
• [Virtualconference] Sparse multiple testing: can one estimate the null distribution ? / Auteur de la Conférence Roquain, Etienne. CIRM
  
• [Virtualconference] Bayesian spatial adaptation / Auteur de la Conférence Rockova, Veronika. CIRM
  
• [Virtualconference] Universal inference using the split likelihood ratio test / Auteur de la Conférence Ramdas, Aaditya K.. CIRM
  
• [Virtualconference] How to estimate a density on a spider web ? / Auteur de la Conférence Picard, Dominique. CIRM
  
• [Virtualconference] Post hoc bounds on false positives using reference families / Auteur de la Conférence Neuvial, Pierre. CIRM
  
• [Virtualconference] Quasi logistic distributions and Gaussian scale mixing / Auteur de la Conférence Letac, Gerard. CIRM
  
• [Virtualconference] Shrinkage estimation of mean for complex multivariate normal distribution with unknown covariance when p > n / Auteur de la Conférence Konno, Yoshihiko. CIRM
  
• [Virtualconference] Treatment effect estimation with missing attributes / Auteur de la Conférence Josse, Julie. CIRM
  
• [Virtualconference] Floodgate: inference for model-free variable importance / Auteur de la Conférence Janson, Lucas. CIRM
  
• [Virtualconference] On Cholesky structures on real symmetric matrices and their applications / Auteur de la Conférence Ishi, Hideyuki. CIRM
  
• [Virtualconference] Optimal control of false discovery criteria in the general two-group model / Auteur de la Conférence Heller, Ruth. CIRM
  
• [Virtualconference] Isotonic Distributional Regression (IDR) - leveraging monotonicity, uniquely so! / Auteur de la Conférence Gneiting, Tilmann. CIRM
  
• [Virtualconference] De-biasing arbitrary convex regularizers and asymptotic normality / Auteur de la Conférence Bellec, Pierre C.. CIRM
  
• [Virtualconference] Consistent model selection criteria and goodness-of-fit test for common time series models / Auteur de la Conférence Bardet, Jean-Marc. CIRM
  
• [Virtualconference] High-dimensional classification by sparse logistic regression / Auteur de la Conférence Abramovich, Felix. CIRM
  

BECK, Amir et TEBOULLE, Marc. Smoothing and first order methods: A unified framework. SIAM Journal on Optimization, 2012, vol. 22, no 2, p. 557-580. - https://doi.org/10.1137/100818327


BELLONI, Alexandre, CHERNOZHUKOV, Victor, et WANG, Lie. Square-root lasso: pivotal recovery of sparse signals via conic programming. Biometrika, 2011, vol. 98, no 4, p. 791-806. - https://www.jstor.org/stable/23076172


BERTRAND, Quentin, MASSIAS, Mathurin, GRAMFORT, Alexandre, et al. Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso. In : Advances in Neural Information Processing Systems. 2019. p. 3961-3972. - https://arxiv.org/abs/1902.02509


BICKEL, Peter J., RITOV, Ya’acov, TSYBAKOV, Alexandre B., et al. Simultaneous analysis of Lasso and Dantzig selector. The Annals of Statistics, 2009, vol. 37, no 4, p. 1705-1732. - http://dx.doi.org/10.1214/08-AOS620


CANDES, E.J., WAKIN, M.B. & BOYD, S.P. Enhancing Sparsity by Reweighted ℓ 1 Minimization. J Fourier Anal Appl 14, 877–905 (2008). - https://doi.org/10.1007/s00041-008-9045-x


CHEN, Scott S. et DONOHO, David L. andM. A. Saunders," Atomic decomposition by basis pursuit,". SIAM J. Sci. Comput, 1999, vol. 20, no 1, p. 33-61. - https://doi.org/10.1137/S1064827596304010


DALALYAN, Arnak S., HEBIRI, Mohamed, LEDERER, Johannes, et al. On the prediction performance of the lasso. Bernoulli, 2017, vol. 23, no 1, p. 552-581. - http://dx.doi.org/10.3150/15-BEJ756


DAUBECHIES, Ingrid. CBMS-NSF regional conference series in applied mathematics. Ten lectures on wavelets, 1992, vol. 61. - https://doi.org/10.1137/1.9781611970104


DELORME, Arnaud, PALMER, Jason, ONTON, Julie, et al. Independent EEG sources are dipolar. PloS one, 2012, vol. 7, no 2. - http://dx.doi.org/10.1371/journal.pone.0030135


MASSIAS, Mathurin, FERCOQ, Olivier, GRAMFORT, Alexandre, et al. Generalized concomitant multi-task lasso for sparse multimodal regression. In: AISTATS. Vol. 84. 2018, p. 998-1007.
- http://proceedings.mlr.press/v84/massias18a/massias18a.pdf


MASSIAS, Mathurin, BERTRAND, Quentin, GRAMFORT, Alexandre, et al. Support recovery and sup-norm convergence rates for sparse pivotal estimation. In: AISTATS. 2020. - https://arxiv.org/abs/2001.05401


NDIAYE, Eugene, FERCOQ, Olivier, GRAMFORT, Alexandre, et al. Efficient smoothed concomitant Lasso estimation for high dimensional regression. In : Journal of Physics: Conference Series. IOP Publishing, 2017. p. 012006. - https://doi.org/10.1088/1742-6596/904/1/012006


NESTEROV, Yu. Smooth minimization of non-smooth functions. Mathematical programming, 2005, vol. 103, no 1, p. 127-152. - https://doi.org/10.1007/s10107-004-0552-5


OBOZINSKI, Guillaume, TASKAR, Ben, et JORDAN, Michael I. Joint covariate selection and joint subspace selection for multiple classification problems. Statistics and Computing, 2010, vol. 20, no 2, p. 231-252. - https://doi.org/10.1007/s11222-008-9111-x


OLSHAUSEN, Bruno A. et FIELD, David J. Sparse coding with an overcomplete basis set: A strategy employed by V1?. Vision research, 1997, vol. 37, no 23, p. 3311-3325. - https://doi.org/10.1016/S0042-6989(97)00169-7


OWEN, A. B. A robust hybrid of lasso and ridge regression. In: Contemporary Mathematics 2007, vol. 443, p. 59–72. - http://dx.doi.org/10.1090/conm/443


TIBSHIRANI, Robert. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 1996, vol. 58, no 1, p. 267-288. - https://www.jstor.org/stable/2346178


VAN DE GEER, Sara. Estimation and testing under sparsity. Lecture notes in mathematics, 2016, vol. 2159. - http://dx.doi.org/10.1007/978-3-319-32774-7