We proposed new variance estimators for the poststratified estimator of the population
total in two-stage sampling. The linearization or Taylor series variance estimator
and the jackknife linearization variance estimator are popular for the poststratified estimator. The jackknife linearization variance estimator utilizes the ratio, ^Rc, which
balances the weights for the poststrata while the linearization or Taylor series estimator
does not. The jackknife linearization variance estimator is equivalent to Rao's
(1985) adjusted variance estimator. Our proposed estimator makes use of the ratio,
^R c, in a different shape which is naturally derived from the process of expanding
to the second-order Taylor series linearization, while the standard linearization variance
estimator is only expanded to the first-order. We investigated the properties
and performance of the linearization variance estimator, the jackknife linearization
estimator, the proposed variance estimator and its modified version analytically and
through simulation study. The simulation study was carried out on both artificially
generated data and real data. The result showed that the second order accurate
variance estimator and its modified version could be very good candidates for the
variance estimation of poststratified estimator of population total.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/5895 |
| Date | 17 September 2007 |
| Creators | Kim, Kyong Ryun |
| Contributors | Wang, Suojin |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | 3885369 bytes, electronic, application/pdf, born digital |
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