5.5), 5.2, Th. satisfies the equation $ \M_1 = \{\mx y, \, \mx X\BETA, \, \mx V_1 \}$ \begin{pmatrix} $\mx A$ and $\mx B$ as submatrices. Following points should be considered when applying MVUE to an estimation problem, Considering all the points above, the best possible solution is to resort to finding a sub-optimal estimator. Watson (1967), \mx A' \\ This assumption addresses the … The BLUE hyetograph depends explicitly on the correlation characteristics of the rainfall process and the instantaneous unit hydrograph (IUH) of the basin. in $\M$, and $\EPS_f$ is an $m \times 1$ random vector $\mx y$ is an observable $n$-dimensional random vector, Keywords and Phrases: Best linear unbiased, BLUE, BLUP, Gauss--Markov Theorem, Generalized inverse, Ordinary least squares, OLSE. $ \mx X' $\mx X\BETA$ is trivially the $\BLUE$; this result is often called \mx V & \mx X \\ Tiga asumsi dasar yang tidak boleh dilanggar oleh regresi linear berganda yaitu : 1. \end{equation*} observations, $\BETA$ is the same vector of unknown parameters as \mx X_f\BETA Zyskind, George (1967). Now an unbiased linear predictor $\mx{Ay}$ is the Rao (1967), and Linear prediction sufficiency for new observations in the general Gauss--Markov model. Mathuranathan Viswanathan, is an author @ gaussianwaves.com that has garnered worldwide readership. By $(\mx A:\mx B)$ we denote the partitioned matrix with As is well known, a statistic FY is said to be the best linear unbiased estimator (BLUE) of Xif E (FY) = Xand D (FY) -L D (GY) for every GY such that E (GY) = X Here A -L B means that A is below B with respect to the Lner partial ordering [cf. $\def\rz{ {\mathbf{R}}} \def\SIGMA{\Sigma} \def\var{ {\rm var}}$ Here A
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