CALIBRATION ESTIMATION USING EMPIRICAL LIKELIHOOD IN SURVEY SAMPLING

Cited 28 time in webofscience Cited 0 time in scopus
  • Hit : 265
  • Download : 0
Calibration estimation, which can be roughly described as a method of adjusting the original design weights to incorporate the known population totals of the auxiliary variables, has become very popular in sample surveys. The calibration weights are chosen to minimize a given distance measure while satisfying a set of constraints related to the auxiliary variable information. Under simple random sampling, Chen and Qin (1993) suggested that the calibration estimator maximizing the constrained empirical likelihood can make efficient use of the auxiliary variables. We extend the result to unequal probability sampling and propose an algorithm to implement the proposed procedure. Asymptotic properties of the proposed calibration estimator are discussed. The proposed method is extended to the stratified sampling. Results from a limited simulation study are presented
Publisher
STATISTICA SINICA
Issue Date
2009-01
Language
English
Article Type
Article
Keywords

AUXILIARY INFORMATION; MODELS

Citation

STATISTICA SINICA, v.19, no.1, pp.145 - 157

ISSN
1017-0405
URI
http://hdl.handle.net/10203/212947
Appears in Collection
MA-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 28 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0