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核型密度估计量的改进
Estimation of the value of a density function at a point of continuity using a kernel-type estimator is discussed and improvements of the technique are presented. The generalized jackknife method is employed to reduce the asymptotic and small sample mean square error and bias of these estimators. The procedure presented has the flexibility to afford the user a choice between bias reduction, variance reduction, or both.
广义刀切法及其与统计微分的关系
The flexibility of the definition of the first-order generalized jackknife is exploited so that its relation to the method of statistical differentials can be seen. The estimators presented have the same bias reduction and asymptotic distributional properties as the usual generalized jackknife. A limiting case produces an infinitesimal jackknife which represents a generalization of statistical differentials.
两组排序一致性检验的若干性质
The statistic L, introduced to test for concordance within and between two groups of rankings of k objects is shown to be related to several measures of internal rank correlation. It has been previously shown that Kendall's W is proportional to the average of the rank correlations of all pairs of rankings in a single group and that Page's L is essentially the average of the rank correlations of an external ranking with each ranking in a group suggested by Lyerly. Similarly L…
广义 jackknife 与统计微分的关系
The flexibility of the definition of the first-order generalized jackknife is exploited so that its relation to the method of statistical differentials can be seen. The estimators presented have the same bias reduction and asymptotic distributional properties as the usual generalized jackknife. A limiting case produces an infinitesimal jackknife which represents a generalization of statistical differentials.
估计中的偏差减少
A general procedure for reducing the bias of point estimators is introduced. The technique includes the “jackknife” as a special case. The existing notion of reapplication is shown to lack a desirable bias removal property for which it was originally designed. Proper reapplication is proposed to conform to the general notion of higher order bias elimination and an interesting algorithm for the correct method is defined. Illustrative examples are drawn from ratio estimation,…