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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
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Mohammad Jafari Jozani   |
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Abstract: (3781 Views) |
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] |
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| Keywords: minimax estimation, restricted parameter space, squared log error loss, binomial distribution, lower bounded parameter space, twopoint prior. |
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Full-Text [PDF 367 kb]
(962 Downloads)
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Type of Study: Research |
Subject:
General
Received: 2016/02/10 | Accepted: 2016/02/10 | Published: 2016/02/10
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