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In the paper the problem of simultaneous linear estimation of fixed and random effects in the mixed linear model is considered. A necessary and sufficient conditions for a linear estimator of a linear function of fixed and random effects in balanced nested and crossed classification models to be admissible are given.
Let X1, X2, ..., Xn be a random sample from a normal distribution with unknown mean μ and known variance σ2. In many practical situations, μ is known a priori to be restricted to a bounded interval, say [−m, m] for some m > 0. The sample mean $$\bar{X}$$ , then, becomes an inadmissible estimator for μ. It is also not minimax with respect to the squared error loss function. Minimax and...
In data-processing standpoint, an efficient algorithm for identifying the minimum value among a set of measurements are record statistics. From a sequence of n independent identically distributed continuous random variables only about log(n) records are expected, so we expect to have little data, hence any prior information is welcome (Houchens, Record value theory and inference, Ph.D. thesis, University...
Consider an estimation problem under the squared-log error loss function in a one-parameter non-regular distribution when the endpoint of the support depends on an unknown parameter. The purpose of this paper is to give sufficient conditions for a generalized Bayes estimator of a parametric function to be admissible. Some examples are given.
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