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Title: Locally and uniformly best estimators in replicated regression model (English)
Author: Volaufová, Júlia
Author: Kubáček, Lubomír
Language: English
Journal: Aplikace matematiky
ISSN: 0373-6725
Volume: 28
Issue: 5
Year: 1983
Pages: 386-390
Summary lang: English
Summary lang: Slovak
Summary lang: Russian
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Category: math
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Summary: The aim of the paper is to estimate a function $\gamma=tr(D\beta\beta')+tr(C\sum)$ (with $d, C$ known matrices) in a regression model $(Y, X\beta,\sum)$ with an unknown parameter $\beta$ and covariance matrix $\sum$. Stochastically independent replications $Y_1,\ldots, Y_m$ of the stochastic vector $Y$ are considered, where the estimators of $X\beta$ and $\sum$ are $\bar{Y}=\frac 1 m \sum ^m _{i=1} Y_i$ and $\hat{\sum}=(m-1)^{-1} \sum^m_{i=1}(Y_i-\bar{Y})(Y_i-\bar{Y})'$, respectively. Locally and uniformly best inbiased estimators of the function $\gamma$, based on $\bar{Y}$ and $\hat{\sum}$, are given. (English)
Keyword: replicated regression model
Keyword: best unbiased estimators
MSC: 62H12
MSC: 62J05
idZBL: Zbl 0529.62056
idMR: MR0712914
DOI: 10.21136/AM.1983.104049
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Date available: 2008-05-20T18:23:15Z
Last updated: 2020-07-28
Stable URL: http://hdl.handle.net/10338.dmlcz/104049
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Reference: [1] Jürgen Kleffe: C. R. Rao's MINQUE for replicated and multivariate observations.Lecture Notes in Statistics 2. Mathematical Statistics and Probability Theory. Proceedings Sixth International Conference. Wisla (Poland) 1978. Springer N. York, Heidelberg, Berlin 1979, 188-200.
Reference: [2] Jürgen Kleffe, Júlia Volaufová: Optimality of the sample variance-covariance matrix in repeated measurement designs.(Submitted to Sankhyā).
Reference: [3] C. R. Rao: Linear Statistical Inference and Its Applications.J. Wiley, N. York 1965. Zbl 0137.36203, MR 0221616
Reference: [4] C. R. Rao S. K. Mitra: Generalized Inverse of Matrices and Its Applications.J. Wiley, N. York 1971. MR 0338013
Reference: [5] R. Thrum J. Kleffe: Inequalities for moments of quadratic forms with applications to a.s. convergence.Math. Operationsforsch. Statistics Ser. Statistics (in print). MR 0704788
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