TY - JOUR
TI - Variable selection in saturated and supersaturated designs via - minimization
AU - Buccini, A.
AU - De la Cruz Cabrera, O.
AU - Koukouvinos, C.
AU - Mitrouli, M.
AU - Reichel, L.
JO - COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
PY - 2021
VL - null
TODO - null
SP - null
PB - Taylor and Francis Ltd.
SN - 0361-0918
TODO - 10.1080/03610918.2021.1961151
TODO - Communication;  Computer simulation;  Statistics, Generalized Krylov subspaces;  Large-scale problem;  Minimization problems;  Practical solutions;  Real-world problem;  Supersaturated designs;  Variable selection;  Variable selection problems, Regression analysis
TODO - In many real world problems it is of interest to ascertain which factors are most relevant for determining a given outcome. This is the so-called variable selection problem. The present paper proposes a new regression model for its solution. We show that the proposed model satisfies continuity, sparsity, and unbiasedness properties. A generalized Krylov subspace method for the practical solution of the minimization problem involved is described. This method can be used for the solution of both small-scale and large-scale problems. Several computed examples illustrate the good performance of the proposed model. We place special focus on screening studies using saturated and supersaturated experimental designs. © 2021 Taylor & Francis Group, LLC.
ER -