Unbiased calibrated estimation on a distribution function in simple random sampling
Author(s)
Date issued
March 24, 2002
In
Survey Methodology
Vol
1
No
28
From page
77
To page
85
Subjects
Unbiased estimation Calibration on a distribution function Conditional inclusion probabilities Weighting
Abstract
The poststratified estimator sometimes has empty strata. To address this problem, we construct a poststratified estimator
with poststrata sizes set in the sample. The post-strata sizes are then random in the population. The next step is to construct a smoothed estimator by calculating a moving average of the poststratified estimators. Using this technique it is possible to
construct an exact theory of calibration on distribution. The estimator obtained is not only calibrated on distribution, it is linear and completely unbiased. We then compare the calibrated estimator with the regression estimator. Lastly, we propose an approximate variance estimator that we validate using simulations.
with poststrata sizes set in the sample. The post-strata sizes are then random in the population. The next step is to construct a smoothed estimator by calculating a moving average of the poststratified estimators. Using this technique it is possible to
construct an exact theory of calibration on distribution. The estimator obtained is not only calibrated on distribution, it is linear and completely unbiased. We then compare the calibrated estimator with the regression estimator. Lastly, we propose an approximate variance estimator that we validate using simulations.
Publication type
journal article