Inference for non-probability samples using the calibration approach for quantiles
arxiv(2024)
摘要
Non-probability survey samples are examples of data sources that have become
increasingly popular in recent years, also in official statistics. However,
statistical inference based on non-probability samples is much more difficult
because they are biased and are not representative of the target population
(Wu, 2022). In this paper we consider a method of joint calibration for totals
(Deville Särndal, 1992) and quantiles (Harms Duchesne, 2006) and use the
proposed approach to extend existing inference methods for non-probability
samples, such as inverse probability weighting, mass imputation and doubly
robust estimators. By including quantile information in the estimation process
non-linear relationships between the target and auxiliary variables can be
approximated the way it is done in step-wise (constant) regression. Our
simulation study has demonstrated that the estimators in question are more
robust against model mis-specification and, as a result, help to reduce bias
and improve estimation efficiency. Variance estimation for our proposed
approach is also discussed. We show that existing inference methods can be used
and that the resulting confidence intervals are at nominal levels. Finally, we
applied the proposed methods to estimate the share of vacancies aimed at
Ukrainian workers in Poland using an integrated set of administrative and
survey data about job vacancies. The proposed approaches have been implemented
in two R packages (nonprobsvy and jointCalib), which were used to conduct the
simulation and empirical study
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