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A jackknife variance estimator for unequal probability sampling
Author(s) -
Berger Yves G.,
Skinner Chris J.
Publication year - 2005
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/j.1467-9868.2005.00489.x
Subject(s) - jackknife resampling , estimator , statistics , consistency (knowledge bases) , variance (accounting) , minimum variance unbiased estimator , mathematics , monte carlo method , sampling (signal processing) , bias of an estimator , probability sampling , efficient estimator , sampling design , econometrics , computer science , accounting , demography , filter (signal processing) , sociology , business , computer vision , population , geometry
Summary. 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.