The effect of target function on the predictive performance of the two‐stage ridge estimator

Abstract

The main thrust of this study is to consider the problem of simultaneous prediction of actual and average values of the simultaneous equations model through the target function of Shalabh (Bulletin of International Statistical Institute, 1995, 56, 1375–1390). We focus on the predictive performance of the two‐stage ridge estimator with the motivation for eliminating the disorder arising from multicollinearity. An optimal biasing parameter of the two‐stage ridge estimator is derived by a minimization process of prediction mean square error. In addition, an optimal estimator for the weight of observed value in target function is attained theoretically. The results inferred from a numerical example and a Monte Carlo experiment provide a dramatic improvement in the predictive ability of the two‐stage ridge estimator.

Analysis of the Implementation Path of Civic Education Work in institution of higher education under the Background of Internet+

“…The CS algorithm is then used to make the initial choices of input weights W and explicit layer bias of the ELM model in an attempt to address the fact that these choices have an impact on the model's performance [27][28]. Equation ( 16) illustrates the objective function of a suggested CS-ELM-based assessment technique for the success of political and ideological instruction at colleges and universities:…”