On Ban Estimators for Chi Squared Test Criteria

Wijsman (1959a) developed the theory of BAN estimators of a parameter $\beta$ under some fairly general conditions assuming that $n^{1/2}(y_n - g(\beta)) \rightarrow_L N_s(0, \sigma(\beta))$ . The present article considers the complementary, but somewhat more general, approach under the constraint equation model that restricts the parameter $\mu$ so that $f(\mu) = 0$ under general conditions requiring $n^{1/2}(y_n - \mu) \rightarrow_L N_s(0, \sigma^\ast(\mu))$ . At the same time, this article weakens Wijsman's differentiability requirement by introducing a $p$ -differentiability condition for regular estimators. Next the theory of BAN estimation is developed for a model combining features of both of these approaches. As a special case of the model above, weighted least squares estimators for a general linear model are shown to be BAN.Wijsman (1959a) developed the theory of BAN estimators of a parameter $\beta$ under some fairly general conditions assuming that $n^{1/2}(y_n - g(\beta)) \rightarrow_L N_s(0, \sigma(\beta))$. The present article considers the complementary, but somewhat more general, approach under the constraint equation model that restricts the parameter $\mu$ so that $f(\mu) = 0$ under general conditions requiring $n^{1/2}(y_n - \mu) \rightarrow_L N_s(0, \sigma^\ast(\mu))$. At the same time, this article weakens Wijsman's differentiability requirement by introducing a $p$-differentiability condition for regular estimators. Next the theory of BAN estimation is developed for a model combining features of both of these approaches. As a special case of the model above, weighted least squares estimators for a general linear model are shown to be BAN.

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笔记与分组

分类依据与纠错

On Ban Estimators for Chi Squared Test Criteria

代码、数据与补充材料

卡方检验准则的 BAN 估计量

原文摘要

同方向的基础文献

同方向的基础文献