Stratified medicine seeks to identify biomarkers or parsimonious gene signatures distinguishing patients that will benefit most from a targeted treatment. We evaluated 12 approaches in high-dimensional Cox models in randomized clinical trials: penalization of the biomarker main effects and biomarker-by-treatment interactions (full-lasso, three kinds of adaptive lasso, ridge+lasso and group-lass...

We consider the problem of predicting an outcome variable using p covariates that are measured on n independent observations, in a setting in which additive, flexible, and interpretable fits are desired. We propose the fused lasso additive model (FLAM), in which each additive function is estimated to be piecewise constant with a small number of adaptively-chosen knots. FLAM is the solution to a...

We consider the least-square linear regression problem with regularization by the l 1-norm, a problem usually referred to as the Lasso. In this paper, we first present a detailed asymptotic analysis of model consistency of the Lasso in low-dimensional settings. For various decays of the regularization parameter, we compute asymptotic equivalents of the probability of correct model selection. Fo...

TO THE EDITOR—The recent article by Salgado-Pabón et al, which reported that excision of the staphylococcal β-toxin– converting bacteriophage wSa3 is common in wSa3-positive strains, was interesting [1]. By demonstrating that β-toxin production is selected for by in vivo infection and enhances virulence in rabbit models of pneumonia and infective endocarditis, Salgado-Pabón et al identified a p...

Lasso is a popular method for variable selection in regression. Much theoretical understanding has been obtained recently on its model selection or sparsity recovery properties under sparse and homoscedastic linear regression models. Since these standard model assumptions are often not met in practice, it is important to understand how Lasso behaves under nonstandard model assumptions. In this ...

We consider the group lasso penalty for the linear model. We note that the standard algorithm for solving the problem assumes that the model matrices in each group are orthonormal. Here we consider a more general penalty that blends the lasso (L1) with the group lasso (“two-norm”). This penalty yields solutions that are sparse at both the group and individual feature levels. We derive an effici...

Reseña de Homero, Odisea, introducción Begoña Ortega Villaro, traducción José Manuel Pabón, Madrid, RBA, Biblioteca Clásica, 2019, 527 páginas, ISBN 978-84-249-3907-6

Previous genome-wide association study (GWAS) focused on low-order interactions between pairwise single-nucleotide polymorphisms (SNPs) with significant main effects. Little is known how high-order interactions effect, especially one among the SNPs without main effects regulates quantitative traits.Within the frameworks of linear model and generalized linear model, the LASSO with coordinate des...

background nowadays, productivity and efficiency are considered a culture and a perspective in both life and work environments. this is the starting point of human development. objectives the aim of the present study was to investigate the performance of hospitals affiliated to mashhad university of medical sciences using the pabon lasso model. methods the present study was a descriptive-analyt...