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Optimally Robust Linear Estimators of Location

位置参数的最优稳健线性估计量

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…

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Asymptotically Robust Estimators of Location

位置参数的渐近稳健估计量

For the problem of efficiency-robust estimation of location, approximate versions are developed of optimally robust Pitman-type estimators. These are shown to have full asymptotic efficiency for a prototype family of distributions, used to define the estimators. The asymptotic efficiency under other distributions of interest is also characterized. For sample sizes n = 20, 30, 40, 50, and 100 the efficiencies were estimated by Monte Carlo methods under the following…

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Optimal Robustness: A General Method, with Applications to Linear Estimators of Location

最优稳健性:一般方法及其在线性位置估计中的应用

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.…

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Efficiency Robust Two-Sample Rank Tests

效率稳健的两样本秩检验

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…

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Efficiency Robust Two-Sample Rank Tests

效率稳健的两样本秩检验

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…

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Optimal Robustness: A General Method, with Applications to Linear Estimators of Location

最优稳健性的一般方法及其在线性位置估计中的应用

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.…

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On the Foundations of Statistical Inference

统计推断的基础

The concept of conditional experimental frames of reference has a significance for the general theory of statistical inference which has been emphasized by R. A. Fisher, D. R. Cox, J. W. Tukey, and others. This concept is formulated as a principle of conditionality, from which some general consequences are deduced mathematically. These include the likelihood principle, which has not hitherto been very widely accepted, in contrast with the conditionality concept which many…

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Confidence Curves: An Omnibus Technique for Estimation and Testing Statistical Hypotheses

置信曲线:统一估计与统计假设检验的方法

A standard practice of physical scientists is to report estimates (“measurements”) accompanied by their standard errors (or alternatively “average errors” or “probable errors”). Such reports are interpreted flexibly as appropriate in various contexts of application. With the usual normality assumption, such reports may be read as representing confidence intervals or limits at the various confidence levels, and this omnibus character largely accounts for the convenience and…

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