RT - Journal Article
T1 - Admissible and Minimax Estimator of the Parameter $theta$ in a Binomial $Bin( n ,theta)$ distribution under Squared Log Error Loss Function in a Lower Bounded Parameter Space
JF - srtc-jsri
YR - 2006
JO - srtc-jsri
VO - 2
IS - 2
UR - http://jsri.srtc.ac.ir/article-1-153-en.html
SP - 129
EP - 140
K1 - minimax estimation
K1 - restricted parameter space
K1 - squared log error loss
K1 - binomial distribution
K1 - lower bounded parameter space
K1 - twopoint prior.
AB - Extended Abstract. The study of truncated parameter space in general is of interest for the following reasons: 1.They often occur in practice. In many cases certain parameter values can be excluded from the parameter space. Nearly all problems in practice have a truncated parameter space and it is most impossible to argue in practice that a parameter is not bounded. In truncated parameter space, the commonly used estimators of $theta$ such as the maximum likelihood estimators are inadmissible. Even more characteristic is the fact that boundary rules are mostly inadmissible, where a boundary estimator is an estimator which takes, with positive probability for some ...[To continue please click here]
LA eng
UL http://jsri.srtc.ac.ir/article-1-153-en.html
M3
ER -