The Asymptotically Unbiased Prior Distribution
Hartigan, J. A.
Ann. Math. Statist., Tome 36 (1965) no. 6, p. 1137-1152 / Harvested from Project Euclid
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In estimation of a real valued parameter $\theta$, using observations from the probability density $f(x \mid \theta)$, and using loss function $L(\theta, \phi)$, the prior density which minimizes asymptotic bias of the associated estimator is shown to be $J(\theta) = \varepsilon((\partial/\partial\theta) \log f)^2/\lbrack(\partial^2/\partial\phi^2)L(\theta, \phi)\rbrack^{\frac{1}{2}}_{\phi = \theta}$. Results are also given for estimation in higher dimensions.
Publié le : 1965-08-14
Classification:  
@article{1177699988,
     author = {Hartigan, J. A.},
     title = {The Asymptotically Unbiased Prior Distribution},
     journal = {Ann. Math. Statist.},
     volume = {36},
     number = {6},
     year = {1965},
     pages = { 1137-1152},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1177699988}
}

Hartigan, J. A. The Asymptotically Unbiased Prior Distribution. Ann. Math. Statist., Tome 36 (1965) no. 6, pp.  1137-1152. http://gdmltest.u-ga.fr/item/1177699988/