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Empirical Likelihood Inference for the Rao‐Hartley‐Cochran Sampling Design
Author(s) -
Berger Yves G.
Publication year - 2016
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/sjos.12200
Subject(s) - mathematics , statistics , sampling design , empirical likelihood , confidence interval , sampling (signal processing) , cdf based nonparametric confidence interval , confidence distribution , robust confidence intervals , coverage probability , inference , poisson sampling , simple random sample , variance (accounting) , confidence region , importance sampling , slice sampling , computer science , artificial intelligence , monte carlo method , business , computer vision , population , demography , filter (signal processing) , sociology , accounting
The Hartley‐Rao‐Cochran sampling design is an unequal probability sampling design which can be used to select samples from finite populations. We propose to adjust the empirical likelihood approach for the Hartley‐Rao‐Cochran sampling design. The approach proposed intrinsically incorporates sampling weights, auxiliary information and allows for large sampling fractions. It can be used to construct confidence intervals. In a simulation study, we show that the coverage may be better for the empirical likelihood confidence interval than for standard confidence intervals based on variance estimates. The approach proposed is simple to implement and less computer intensive than bootstrap. The confidence interval proposed does not rely on re‐sampling, linearization, variance estimation, design‐effects or joint inclusion probabilities.