Calibrated propensity score method for survey nonresponse in cluster sampling

Cited 9 time in webofscience Cited 0 time in scopus
  • Hit : 176
  • Download : 0
Weighting adjustment is commonly used in survey sampling to correct for unit nonresponse. In cluster sampling, the missingness indicators are often correlated within clusters and the response mechanism is subject to cluster-specific nonignorable missingness. Based on a parametric working model for the response mechanism that incorporates cluster-specific nonignorable missingness, we propose a method of weighting adjustment. We provide a consistent estimator of the mean or totals in cases where the study variable follows a generalized linear mixed-effects model. The proposed method is robust in the sense that the consistency of the estimator does not require correct specification of the functional forms of the response and outcome models. A consistent variance estimator based on Taylor linearization is also proposed. Numerical results, including a simulation and a real-data application, are presented
Publisher
OXFORD UNIV PRESS
Issue Date
2016-06
Language
English
Article Type
Article
Keywords

PARAMETRIC FRACTIONAL IMPUTATION; MISSING DATA; CAUSAL INFERENCE; INCOMPLETE DATA; MODEL; ROBUSTNESS

Citation

BIOMETRIKA, v.103, no.2, pp.461 - 473

ISSN
0006-3444
DOI
10.1093/biomet/asw004
URI
http://hdl.handle.net/10203/212983
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 9 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0