Iterative estimators of parameters in linear models with partially variant coefficients

Shaolin Hu; Karl Meinke; Rushan Chen; Ouyang Huajiang

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Multistage regression model

Lubomír Kubáček (1986)

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Necessary and sufficient conditions are given under which the best linear unbiased estimator (BLUE) ${\hat{β}}_{i} (Y_{1}, \dots, Y_{i})$ is identical with the BLUE ${\hat{β}}_{i} ({\hat{β}}_{1}, \dots, {\hat{β}}_{i - 1}, Y_{i})$ ; $Y_{1} \dots, Y_{i}$ are subvectors of the random vector $Y$ in a general regression model $(Y, X β, \sum)$ , ${(β_{1}^{'}, \dots, β_{i}^{'})}^{'} = β$ a vector of unknown parameters; the design matrix $X$ having a special so called multistage struture and the covariance matrix $\sum$ are given.

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(1993)

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A Determinantal Inequality in Hilbert Space.

Shen-Chi Tang (1965)

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The extremal ranks of $A_{1} - B_{1} X C_{1}$ subject to a pair of matrix equations

Yongge Tian, Yonghui Liu (2006)

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Displaying similar documents to “Iterative estimators of parameters in linear models with partially variant coefficients”

One of the calibration problems

Underparametrization in a regression model with constraints II

Recursive method for the two dimensional finite Ising model

Multistage regression model

Erratum

A Determinantal Inequality in Hilbert Space.

The extremal ranks of A 1 - B 1 X C 1 subject to a pair of matrix equations

The extremal ranks of $A_{1} - B_{1} X C_{1}$ subject to a pair of matrix equations