论文检索

位置参数的最优稳健线性估计量AI 解读

Efficiency-robust linear estimation as outlined in Birnbaum and Laska (JASA, 62 (1967) pp. 1230–1240) is applied, determining maximin-efficient linear unbiased estimators (MLUEs), and their efficiencies, for ordered samples of sizes 5 (5) 20 from a family of nine distributions. The results are compactly summarized because of the tendency of these distributions to admit simple orderings such that the MLUE over any subset of the distributions is just the MLUE over the extreme…

最优稳健性：一般方法及其在线性位置估计中的应用AI 解读

A general method of determining efficiency-robust estimation and testing methods is presented. This is used to derive admissible linear unbiased estimators (ALUEs) having the following optimality property: Over a specified (finite) family of possible symmetric shapes of the error distribution, the estimator's respective variances cannot be jointly improved. Such estimators are generalizations of Lloyd's best linear unbiased estimators (BLUEs) determined for single shapes.…

效率稳健的两样本秩检验AI 解读

In the classical two sample problem, the null hypothesis of identical distribution functions is tested. Rank testing procedures have the property that they are valid (have Type I error identically equal to α) for any absolutely continuous distribution function. Choices among rank tests are usually based upon local power calculations for a given specified null distribution against specific parametric alternatives. A rank test is said to be efficiency robust when no other has…

效率稳健的两样本秩检验AI 解读

In the classical two sample problem, the null hypothesis of identical distribution functions is tested. Rank testing procedures have the property that they are valid (have Type I error identically equal to α) for any absolutely continuous distribution function. Choices among rank tests are usually based upon local power calculations for a given specified null distribution against specific parametric alternatives. A rank test is said to be efficiency robust when no other has…

最优稳健性的一般方法及其在线性位置估计中的应用AI 解读

A general method of determining efficiency-robust estimation and testing methods is presented. This is used to derive admissible linear unbiased estimators (ALUEs) having the following optimality property: Over a specified (finite) family of possible symmetric shapes of the error distribution, the estimator's respective variances cannot be jointly improved. Such estimators are generalizations of Lloyd's best linear unbiased estimators (BLUEs) determined for single shapes.…