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The LASSO for generic design matrices as a function of the relaxation parameter
linear regression LASSO relaxation parameter
2011/6/16
The LASSO is a variable subset selection procedure in statistical
linear regression based on ℓ1 penalization of the least-squares
operator. Its behavior crucially depends, both in practice and...
Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions
Noisy matrix decomposition via convex relaxation high dimensions
2011/3/24
We analyze a class of estimators based on convex relaxation for solving high-dimensional matrix decomposition problems. The observations are the noisy realizations of the sum of an (appproximately) lo...
Relaxation Penalties and Priors for Plausible Modeling of Nonidentified Bias Sources
Bias biostatistics causality epidemiology measurement error misclassification observational studies odds ratio relative risk
2010/3/9
In designed experiments and surveys, known laws or de-
sign feat ures provide checks on the most relevant aspects of a model
and identify the target parameters. In contrast, in most observational
s...
Using the Eigenvalue Relaxation for Binary Least-Squares Estimation Problems
Eigenvalue Relaxation Binary Least-Squares Estimation Problems
2010/3/18
The goal of this paper is to survey the properties of the eigenvalue relaxation for least squares binary problems. This relaxation is a convex program which is obtained as the Lagrangian dual of the o...
