2 citations · 2 across the 6 of their papers we have counts for
6 papers
Lasso and elastic nets by orthants
Hugo Maruri-Aguilar
We propose a new method for computing the lasso path, using the fact that the Manhattan norm of the coefficient vector is linear over every orthant of the parameter space. We use s…
Lasso for hierarchical polynomial models
Hugo Maruri-Aguilar, Simon Lunagomez
In a polynomial regression model, the divisibility conditions implicit in polynomial hierarchy give way to a natural construction of constraints for the model parameters. We use th…
The algebraic method in experimental design
Hugo Maruri-Aguilar, Henry P. Wynn
The algebraic method provides useful techniques to identify models in designs and to understand aliasing of polynomial models. The present note surveys the topic of Gröbner bases i…
Smooth supersaturated models
Ron A. Bates, Hugo Maruri-Aguilar, Henry P. Wynn
In areas such as kernel smoothing and non-parametric regression there is emphasis on smooth interpolation and smooth statistical models. Splines are known to have optimal smoothnes…
Minimal average degree aberration and the state polytope for experimental designs
Yael Berstein, Hugo Maruri-Aguilar, Shmuel Onn +2
For a particular experimental design, there is interest in finding which polynomial models can be identified in the usual regression set up. The algebraic methods based on Groebner…
Nonlinear Matroid Optimization and Experimental Design
Yael Berstein, Jon Lee, Hugo Maruri-Aguilar +4
We study the problem of optimizing nonlinear objective functions over matroids presented by oracles or explicitly. Such functions can be interpreted as the balancing of multi-crite…