برآورد انقباض و انتخاب متغیر در مدل های رگرسیون چندگانه با اشتباهات خود کاهشی ضریب تصادفی

In this paper, we consider improved estimation strategies for the parameter vector in multiple regression models with first-order random coefficient autoregressive errors (RCAR(1)). We propose a shrinkage estimation strategy and implement variable selection methods such as lasso and adaptive lasso strategies. The simulation results reveal that the shrinkage estimators perform better than both lasso and adaptive lasso when and only when there are many nuisance variables in the model.