University of Tsukuba. Graduate School of Systems and Information Engineering. Doctoral Program in Social Systems & Management
Abstract
We consider the regression model with variance of random errors havingunequal values in diagonal elements. If variance of random errors has unequalvalues, the results of ordinary linear regression and kernel principal componentregression become inappropriate to be used. The weighted least-squares (WLS)is widely used to handle the limitations. However, WLS on linear regressionyields a linear prediction model and has no guarantee that multicollinearitydoes not exist in the WLS model. In this paper, we propose a method and analgorithm to overcome these di±culties. Then, we compare the proposed modelwith some other methods
