Variable selection is very important in statistical modeling. We are frequently not only interested in using a model for prediction but also need to correctly identify the relevant variables, that is, to recover the correct model under given assumptions. It is known that under certain conditions, the ordinary least squares (OLS) method produces poor prediction results and does not yield a parsimonious model causing overfitting. Therefore the objective of the variable selection methods is to find the variables which are the most relevant for prediction. Such methods are particularly important when the true underlying model has a sparse representation (many parameters close to zero). The identification of relevant variables will reduce the noise and therefore improve the prediction performance of the fitted model.