Τίτλος:
Variable selection in saturated and supersaturated designs via - minimization
Γλώσσες Τεκμηρίου:
Αγγλικά
Περίληψη:
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.
Συγγραφείς:
Buccini, A.
De la Cruz Cabrera, O.
Koukouvinos, C.
Mitrouli, M.
Reichel, L.
Περιοδικό:
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
Εκδότης:
Taylor and Francis Ltd.
Λέξεις-κλειδιά:
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
DOI:
10.1080/03610918.2021.1961151
