This thesis is a pilot study on the subsampling methods for robust estimation in regression models when there are possible outliers in the data. Two basic proposals of the subsampling method are investigated. The main idea is to identify good data points through fitting the model to randomly chosen subsamples. Subsamples containing no outliers are identified by good fit and they are combined to form a subset of good data points. Once the combined sets containing only good data points are identified, classical estimation methods such as the least-squares method and the maximum likelihood method can be applied to do regression analysis using the combined set. Numerical evidence suggest that the subsampling method is robust against outliers with high breakdown point, and it is competitive to other robust methods in terms of both robustness and efficiency. It has wide application to a variety of regression models including the linear regression models, the non-linear regression models and the generalized linear regression models. Research is ongoing with the aim of making this method an effective and practical method for robust inference on regression models.