Resampling methods and their applications in liu regression

Resampling methods are used to provide estimates relying on the underlying data, which helped make estimates for statistics that could not have been before, or double check the estimates obtained using other estimation methods. In this thesis, the application of resampling methods on the linear regression, especially the Liu estimator (LE), is explored. First, diverse resampling methods are discussed. Next, the application of the resampling methods as a tool to estimate the confidence interval (CI) of the Liu regression parameters (LPs) is proposed. After that, the Bootstrap method and cross-validation are used to find the optimal value of the Liu biasing parameter, and in the last chapter the resampling methods are employed to show the effects of different methods of estimating the Liu parameter (LP) on the CI that are estimated using different CI estimation methods. In the conclusion, a comprehensive discussion of the key findings and conclusions is provided.