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A Jackknife Variance Estimator for Unistage Stratified Samples with Unequal Probabilities

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  • Yves G. Berger

Abstract

Existing jackknife variance estimators used with sample surveys can seriously overestimate the true variance under unistage stratified sampling without replacement with unequal probabilities. A novel jackknife variance estimator is proposed which is as numerically simple as existing jackknife variance estimators. Under certain regularity conditions, the proposed variance estimator is consistent under stratified sampling without replacement with unequal probabilities. The high entropy regularity condition necessary for consistency is shown to hold for the Rao--Sampford design. An empirical study of three unequal probability sampling designs supports our findings. Copyright 2007, Oxford University Press.

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  • Yves G. Berger, 2007. "A Jackknife Variance Estimator for Unistage Stratified Samples with Unequal Probabilities," Biometrika, Biometrika Trust, vol. 94(4), pages 953-964.
    • Handle: RePEc:oup:biomet:v:94:y:2007:i:4:p:953-964
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    File URL: http://hdl.handle.net/10.1093/biomet/asm072
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    Cited by:

    1. Sixia Chen & David Haziza, 2017. "Multiply robust imputation procedures for zero-inflated distributions in surveys," METRON, Springer;Sapienza Università di Roma, vol. 75(3), pages 333-343, December.

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