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截断参数空间中线性不变估计量的不可容许性AI 解读

Estimation problems are considered where the actual parameter space is a strict subset of the full parameter space. A linear invariance structure is assumed and a quadratic loss function is used. Then it is shown that estimators taking values near the boundary of the parameter space are inadmissible and better estimators are presented. Applications are given in the fields of inequality constraint regression and randomized response.

截断参数空间中线性不变估计量的不可容许性AI 解读

Estimation problems are considered where the actual parameter space is a strict subset of the full parameter space. A linear invariance structure is assumed and a quadratic loss function is used. Then it is shown that estimators taking values near the boundary of the parameter space are inadmissible and better estimators are presented. Applications are given in the fields of inequality constraint regression and randomized response.