Kuran, ÖzgeYalaz, Seçil2024-04-172024-04-172022Kuran, Ö. ve Yalaz, S. (2022). Kernel Liu prediction approach in partially linear mixed measurement error models, Statistics, 56(6), 1385-1408.0233-1888https://www.tandfonline.com/doi/epdf/10.1080/02331888.2022.2152816?needAccess=truehttps://hdl.handle.net/11468/13904In this paper, we put forward ‘kernel Liu prediction approach’ instead of ‘kernel prediction approach’ under multicollinearity case in partially linear mixed measurement error model. We obtain the necessary and sufficient condition for the superiority of the linear combinations of the predictors in the sense of the matrix mean square error criterion and give the selection of the Liu biasing parameter via the Conceptual Prediction ((Formula presented.)) criterion. The asymptotic normality condition is examined and the unknown covariance matrix of measurement errors circumstance is derived. We study a numerical example together with a Monte Carlo simulation study to evaluate the performance of the kernel Liu prediction approach at the end of this paper.eninfo:eu-repo/semantics/closedAccessAsymptotic normalityKernel Liu predictionMeasurement errorMulticollinearityPartially linear mixed modelArticle56613851408WOS:0008935813000012-s2.0-8514335840010.1080/02331888.2022.2152816Q4Q4