This paper develops theoretical results regarding noisy 1-bit compressed sensing and sparse binomial regression. We demonstrate that a single convex program gives an accurate estim...
Background: Growing interest on biological pathways has called for new statistical methods for modeling and testing a genetic pathway effect on a health outcome. The fact that gen...
The bootstrap resampling method may be efficiently used to estimate the generalization error of a family of nonlinear regression models, as artificial neural networks. The main dif...
Geoffroy Simon, Amaury Lendasse, Vincent Wertz, Mi...
We present an algorithm to estimate the parameters of a linear model in the presence of heteroscedastic noise, i.e., each data point having a different covariance matrix. The algor...
The Total Least Squares method is generalized in the context of the functional linear model. A smoothing splines estimator of the functional coefficient of the model is first prop...