Picture of virus under microscope

Research under the microscope...

The Strathprints institutional repository is a digital archive of University of Strathclyde research outputs.

Strathprints serves world leading Open Access research by the University of Strathclyde, including research by the Strathclyde Institute of Pharmacy and Biomedical Sciences (SIPBS), where research centres such as the Industrial Biotechnology Innovation Centre (IBioIC), the Cancer Research UK Formulation Unit, SeaBioTech and the Centre for Biophotonics are based.

Explore SIPBS research

A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm

Harrop Galvao, Roberto Kawakami and Ugulino Araujo, Mario Cesar and Fragoso, Wallace Duarte and Silva, Edvan Cirino and Jose, Gledson Emidio and Carreiro Soares, Sofacles Figueredo and Paiva, Henrique Mohallem (2008) A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm. Chemometrics and intelligent laboratory systems, 92 (1). pp. 83-91. ISSN 0169-7439

Full text not available in this repository. (Request a copy from the Strathclyde author)

Abstract

The successive projections algorithm (SPA) is a variable selection technique designed to minimize collinearity problems in multiple linear regression (MLR). This paper proposes a modification to the basic SPA formulation aimed at further improving the parsimony of the resulting MLR model. For this purpose, an elimination procedure is incorporated to the algorithm in order to remove variables that do not effectively contribute towards the prediction ability of the model as indicated by an F-test. The utility of the proposed modification is illustrated in a simulation study, as well as in two application examples involving the analysis of diesel and com samples by near-infrared (NIR) spectroscopy. The results demonstrate that the number of variables selected by SPA can be reduced without significantly compromising prediction performance. In addition, SPA is favourably compared with classic Stepwise Regression and full-spectrum PLS. A graphical user interface for SPA is available at www.ele.ita.br/similar to kawakami/spa/. (C) 2008 Elsevier B.V. All rights reserved.