On Predictors and Estimators under a Constrained Partitioned Linear Model and its Reduced Models

Melek Eriş Büyükkaya, Nesrin Güler
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Abstract

In this study, we consider a partitioned linear model with linear partial parameter constrains, known as a constrained partitioned linear model (CPLM), and its reduced models. A group of formulas on best linear unbiased predictors (BLUPs) and best linear unbiased estimators (BLUEs) in CPLM is derived via some quadratic matrix optimization methods, and further many basic properties of the predictors and estimators are established under some general assumptions. Our main purpose is to derive various inequalities and equalities for the comparison of covariance matrices of BLUPs and BLUEs under CPLM and its reduced models.
约束分割线性模型及其简化模型下的预测量和估计量
在本研究中,我们考虑了一个具有线性部分参数约束的分割线性模型,即约束分割线性模型(constrained partitioned linear model, CPLM)及其简化模型。利用二次矩阵优化方法导出了CPLM中最优线性无偏预测量和最优线性无偏估计量的一组公式,并在一些一般假设下建立了预测量和估计量的许多基本性质。我们的主要目的是推导出在CPLM及其简化模型下blup和BLUEs协方差矩阵比较的各种不等式和等式。
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