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非平衡混合模型中的参数稳健估计
This paper describes a method of robust estimation of shift and scale parameters in a mixed unbalanced interlaboratory model. Estimators presented result from "easily computable" Fréchet differentiable functionals which enjoy some optimal properties in a small neighborhood of the model. A rigorous treatment of their asymptotic behaviour under departures from the model assumptions and a simulation study are given.
通过子模型构造方差分量的可容许估计量
Simultaneous estimation of the vector of variance components under unbalanced mixed models w.r.t. the ordinary quadratic loss function is considered. A new method of constructing invariant quadratic admissible estimators, both with and without the condition of unbiasedness, is presented. Using this method, admissible estimators for the factorial models with imbalance at the last stage and the unbalanced $(p - 1)$-way nested factors design are constructed.
伽马尺度参数向量的全部可容许线性估计量及随机效应应用
The paper is devoted to the problem of simultaneous estimation of scale and natural parameters of the multiparameter gamma distribution under a quadratic loss. The vector of the scale parameters is assumed to range over a certain subset of the Cartesian product $\mathscr{R}^n_+$ of $n$ positive half lines. We identify the class of all linear admissible estimators for the scale parameters and show that all linear estimators of the natural parameters are inadmissible. Since…