Geodesic
Bayes unbiased estimators of parameters of linear trend with autoregressive errors
Applications of Mathematics, Tome 32 (1987) no. 6, pp. 451-458.

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The method of least wquares is usually used in a linear regression model $\bold {Y=X\beta+\epsilon}$ for estimating unknown parameters $\bold \beta$. The case when $\epsilon$ is an autoregressive process of the first order and the matrix $\bold X$ corresponds to a linear trend is studied and the Bayes approach is used for estimating the parameters $\bold \beta$. Unbiased Bayes estimators are derived for the case of a small number of observations. These estimators are compared with the locally best unbiased ones and with the usual least squares estimators.
DOI : 10.21136/AM.1987.104276
Classification : 62F10, 62F15, 62J05, 62M10
Keywords: autoregressive process of first order; linear trend; Unbiased Bayes estimators; locally best unbiased; least squares estimators 
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     author = {\v{S}tulajter, Franti\v{s}ek},
     title = {Bayes unbiased estimators of parameters of linear trend with autoregressive errors},
     journal = {Applications of Mathematics},
     pages = {451--458},
     publisher = {mathdoc},
     volume = {32},
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     year = {1987},
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     zbl = {0632.62091},
     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.21136/AM.1987.104276/}
}
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Štulajter, František. Bayes unbiased estimators of parameters of linear trend with autoregressive errors. Applications of Mathematics, Tome 32 (1987) no. 6, pp. 451-458. doi : 10.21136/AM.1987.104276. http://geodesic.mathdoc.fr/articles/10.21136/AM.1987.104276/

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