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九种有限总体均值两阶段比率估计量的效率AI 解读

Nine two-phase ratio estimators for the mean of a finite population are presented. Assuming a linear model, exact expressions for their biases and mean squared errors are derived. Then the nine estimators are compared as to bias and mean squared error. The list of estimators includes the Jackknife estimator and four modifications; two of the proposed modifications are implemented by first expressing the classical estimator in the form of a regression estimator.

九种有限总体均值两阶段比率估计量的效率AI 解读

Nine two-phase ratio estimators for the mean of a finite population are presented. Assuming a linear model, exact expressions for their biases and mean squared errors are derived. Then the nine estimators are compared as to bias and mean squared error. The list of estimators includes the Jackknife estimator and four modifications; two of the proposed modifications are implemented by first expressing the classical estimator in the form of a regression estimator.

有限总体中的两阶段比率估计量AI 解读

Modified ratio estimators for the mean are proposed for the case in which the first- and second-phase samples are drawn without replacement and independent of each other. Conditions for these estimators to be more efficient than the classical estimator are investigated. Two methods for evaluating the expectation of the reciprocal of the number of distinct units are presented.

有限总体中的两阶段比率估计量AI 解读

Modified ratio estimators for the mean are proposed for the case in which the first- and second-phase samples are drawn without replacement and independent of each other. Conditions for these estimators to be more efficient than the classical estimator are investigated. Two methods for evaluating the expectation of the reciprocal of the number of distinct units are presented.

比率估计量的小样本结果AI 解读

The relative efficiencies of five estimators for a ratio and the stabilities of three variance estimators are evaluated under a particular model. Results are exact for any sample size.

有限总体均值的广义多元估计量AI 解读

Just as ratio estimators are often indicated when the variable y is positively correlated with an auxiliary variable x so product estimators are indicated when y is negatively correlated with x. The method of estimation suggested by Olkin [4] is extended to the case where some or all of the auxiliary variables are positively correlated and some or all are negatively correlated with y.