A Graphical and Numerical Method for Selection of Variables in Linear Models

Munawar Iqbal, Asghar Ali

Abstract


A model is usually only an approximation of underlying reality. To access this reality in an adequate way, research all over the world, in different dimensions, is in progress. Most of the diagnostic methods that are being used for the selection of variables to retain in the final model are either based on theoretical methods or they are graphical, that is why model assessing becomes difficult. As a result, the regressors in a model may get very large or very small in their number. The researcher, therefore, has to look at variety of options, and has to fit a lot of models and then is found muddled with the choice to which to select and which to reject. This work is based upon introducing a diagnostic procedure for subset selection due to which one may be successful in reducing the number of possible models to be fitted. This strategy consists of graphical as well as numerical measures; this combination helps much in reducing the number of regressors in the model as well as the number of models. We have also introduced some new approaches and thus a considerable reduction in the regressors by this method does not prohibit the researcher to include regressors of his own interest.

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DOI: http://dx.doi.org/10.18187/pjsor.v2i2.95

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Title

A Graphical and Numerical Method for Selection of Variables in Linear Models

Keywords

-

Description

A model is usually only an approximation of underlying reality. To access this reality in an adequate way, research all over the world, in different dimensions, is in progress. Most of the diagnostic methods that are being used for the selection of variables to retain in the final model are either based on theoretical methods or they are graphical, that is why model assessing becomes difficult. As a result, the regressors in a model may get very large or very small in their number. The researcher, therefore, has to look at variety of options, and has to fit a lot of models and then is found muddled with the choice to which to select and which to reject. This work is based upon introducing a diagnostic procedure for subset selection due to which one may be successful in reducing the number of possible models to be fitted. This strategy consists of graphical as well as numerical measures; this combination helps much in reducing the number of regressors in the model as well as the number of models. We have also introduced some new approaches and thus a considerable reduction in the regressors by this method does not prohibit the researcher to include regressors of his own interest.

Date

2006-07-01

Identifier


Source

Pakistan Journal of Statistics and Operation Research; Vol 2. No. 2, July 2006



Print ISSN: 1816-2711 | Electronic ISSN: 2220-5810