A fast algorithm for non-negativity model selection

Authors:
Cristian Gatu;Erricos John Kontoghiorghes
Affiliations:
Department of Public and Business Administration, University of Cyprus, Nicosia, Cyprus and Faculty of Computer Science, “Alexandru Ioan Cuza” University, Iaşi, Romania;Faculty of Management and Economics, Cyprus University of Technology, Lemesos, Cyprus and School of Economics and Finance, Queen Mary, University of London, London, UK
Venue:
Statistics and Computing
Year:
2013

Citing 7
Cited 0

All possible subset regressions using the QR decomposition

Computational Statistics & Data Analysis
Matrix computations (3rd ed.)

Matrix computations (3rd ed.)
Simultaneous estimation in a restricted linear model

Journal of Multivariate Analysis
Regressions by leaps and bounds

Technometrics
Interior-point methods

Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 Vol. IV: optimization and nonlinear equations
Parallel algorithms for computing all possible subset regression models using the QR decomposition

Parallel Computing - Special issue: Parallel computing in numerical optimization
A graph approach to generate all possible regression submodels

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

An efficient optimization algorithm for identifying the best least squares regression model under the condition of non-negative coefficients is proposed. The algorithm exposits an innovative solution via the unrestricted least squares and is based on the regression tree and branch-and-bound techniques for computing the best subset regression. The aim is to filling a gap in computationally tractable solutions to the non-negative least squares problem and model selection. The proposed method is illustrated with a real dataset. Experimental results on real and artificial random datasets confirm the computational efficacy of the new strategy and demonstrates its ability to solve large model selection problems that are subject to non-negativity constrains.