A jackknife variance estimator for unequal probability sampling

The jackknife method is often used for variance estimation in sample surveys but has only been developed for a limited class of sampling designs. We propose a jackknife variance estimator which is defined for any without-replacement unequal probability sampling design. We demonstrate design consiste...

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Veröffentlicht in:	Journal of the Royal Statistical Society. Series B, Statistical methodology Statistical methodology, 2005-02, Vol.67 (1), p.79-89
Hauptverfasser:	Berger, Yves G., Skinner, Chris J.
Format:	Artikel
Sprache:	eng
Schlagworte:	Consistent estimators Estimating techniques Estimation Estimators Exact sciences and technology Experimental design Inclusion probabilities Jackknife resampling Linear inference, regression Linearization Mathematical expressions Mathematics Monte Carlo simulation Parametric inference Point estimators Population estimates Probability Probability and statistics Pseudovalues Sampling Sampling bias Sampling studies Sciences and techniques of general use Smooth function of means Statistical methods Statistical variance Statistics Stratification Studies Variance analysis
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Beschreibung
Zusammenfassung:	The jackknife method is often used for variance estimation in sample surveys but has only been developed for a limited class of sampling designs. We propose a jackknife variance estimator which is defined for any without-replacement unequal probability sampling design. We demonstrate design consistency of this estimator for a broad class of point estimators. A Monte Carlo study shows how the proposed estimator may improve on existing estimators.
ISSN:	1369-7412 1467-9868
DOI:	10.1111/j.1467-9868.2005.00489.x