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Let us do the twist again

机译:让我们再来一次

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摘要

Kr?mer (Sankhyā 42:130-131, 1980) posed the following problem: "Which are the y, given X and V, such that OLS and Gauss-Markov are equal?". In other words, the problem aimed at identifying those vectors y for which the ordinary least squares (OLS) and Gauss-Markov estimates of the parameter vector β coincide under the general Gauss-Markov model y=Xβ + u. The problem was later called a "twist" to Kruskal's Theorem, which provides conditions necessary and sufficient for the OLS and Gauss-Markov estimates of β to be equal. The present paper focuses on a similar problem to the one posed by Kr?mer in the aforementioned paper. However, instead of the estimation of β, we consider the estimation of the systematic part β, which is a natural consequence of relaxing the assumption that X and Y are of full (column) rank made by Kr?mer. Further results, dealing with the Euclidean distance between the best linear unbiased estimator (BLUE) and the ordinary least squares estimator (OLSE) of Xβ, as well as with an equality between BLUE and OLSE are also provided. The calculations are mostly based on a joint partitioned representation of a pair of orthogonal projectors.
机译:Kr?mer(Sankhyā42:130-131,1980)提出了以下问题:“给定X和V,y等于哪个,使得OLS和高斯-马尔可夫相等?”。换句话说,该问题旨在识别在一般的Gauss-Markov模型y =Xβ+ u下参数向量β的普通最小二乘(OLS)和Gauss-Markov估计重合的那些向量y。该问题后来被称为“克鲁斯定理”的“扭曲”,该定理提供了β的OLS和Gauss-Markov估计相等的必要条件和充分条件。本论文着眼于与上述论文中的克尔默提出的问题类似的问题。但是,我们不考虑β的估计,而是考虑系统部分β的估计,这是放宽X和Y完全由Kr?mer提出的假设的自然结果。还提供了进一步的结果,该问题涉及Xβ的最佳线性无偏估计量(BLUE)和普通最小二乘估计量(OLSE)之间的欧几里得距离,以及BLUE和OLSE之间的相等性。该计算主要基于一对正交投影仪的联合分区表示。

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