A confidence interval for the median of a finite population under unequal probability sampling: A model-assisted approach

2007 ◽  
Vol 137 (7) ◽  
pp. 2429-2438
Author(s):  
Suzanne R. Dubnicka
Keyword(s):  
Confidence Interval ◽  
Finite Population ◽  
Probability Sampling ◽  
Unequal Probability Sampling ◽  
Unequal Probability

scholarly journals Confidence Intervals of Gini Coefficient Under Unequal Probability Sampling

2020 ◽  
Vol 36 (2) ◽  
pp. 237-249
Author(s):  
Yves G. Berger ◽  
İklim Gedik Balay
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Empirical Likelihood ◽  
Simulation Study ◽  
Gini Coefficient ◽  
Probability Sampling ◽  
Unequal Probability Sampling ◽  
Unequal Probability ◽  
The Gini Coefficient

AbstractWe propose an estimator for the Gini coefficient, based on a ratio of means. We show how bootstrap and empirical likelihood can be combined to construct confidence intervals. Our simulation study shows the estimator proposed is usually less biased than customary estimators. The observed coverages of the empirical likelihood confidence interval proposed are also closer to the nominal value.

Evaluation of sampling alternatives to quantify tree leaf area

2003 ◽  
Vol 33 (1) ◽  
pp. 82-95 ◽  
Author(s):  
H Temesgen
Keyword(s):  
Leaf Area ◽  
Poor Performance ◽  
Tree Crown ◽  
Two Stage ◽  
Zero Bias ◽  
Probability Sampling ◽  
First Order ◽  
Unequal Probability Sampling ◽  
Unequal Probability ◽  
Tree Leaf

High within- and among-tree crown variation have contributed to the difficulty of tree-crown sampling and single-tree leaf area (area available for photosynthesis) estimation. Using reconstructed trees, simulations were used to compare five sampling designs for bias, mean square error (MSE), and distribution of the estimates. All sampling designs showed nearly zero bias. For most sample trees, stratified random sampling resulted in the lowest MSE values, followed by ellipsoidal, two-stage systematic, simple random, and then by two-stage unequal probability sampling. The poor performance of two-stage unequal probability sampling can be ascribed to the unequal probability of inclusion of first-order branches and twigs.

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