Variance estimation under monotone non-response for a panel survey
Section 7. Conclusion
In this paper, we considered variance estimation
accounting for weighting adjustments in panel surveys. We proposed both an
approximately unbiased variance estimator and a simplified variance estimator
for estimators of totals, complex parameters and measures of change, which
covers most cases that may be encountered in practice. Our simulation results
indicate that the proposed variance estimator performs well in all cases
considered. The simplified variance estimator tends to overestimate the
variance of the expansion estimator for totals, and to overestimate the
variance for calibrated estimators of totals when the calibration variables
lack of explanatory power for the variable of interest. However, the simplified
variance estimator performs well for the estimation of ratios and change in
totals with calibrated weights, even if the calibration model is not
appropriate for the study variable.
The assumption of independent response behaviour is
usually not tenable for multi-stage surveys, since units within clusters tend
to be correlated with respect to the response behaviour. In this context,
estimation of response probabilities based upon conditional logistic regression
in the context of correlated responses has been studied by Skinner and D’Arrigo
(2011), see also Kim, Kwon and Park (2016). Extending the present work in the
context of correlated response behaviour is a challenging problem for further
research.
Acknowledgements
We thank the Editors, an Associate Editor and the
referees for useful comments and suggestions which led to an improvement of the
paper.
Appendix
Estimation of the variance due to non-response
for Response Homogeneity Groups
We consider the model of Response Homogeneity Groups
introduced in Section 2.5. Recall that this model may be summarized as follows:
at each time
the sub-sample
is partitioned into
groups
The response probabilities are assumed to be
constant within the groups.
This model is equivalent to the logistic regression
model in (2.18), with
The equation (2.2) leads to the estimated response probabilities
We first consider the case when the reweighted estimator
is computed at time
In the estimator of the variance due to
non-response given in (2.21), the vector
simplifies as
After some algebra, the variance estimator in (2.21) may be rewritten as
We now consider the case when the reweighted estimator
is computed at time
We focus on the simpler case when the same
system of RHGs is kept over time. In the estimator of the variance due to
non-response given in (2.22), the vectors
and
simplify as
After some algebra, the variance estimator in (2.22) may be rewritten as
If we further assume that
is constant over times
and may thus be rewritten as
the expression in (A.7) simplifies as
with
for
This simplification of the variance estimator
can be extended to the reweighted estimator at time
Assuming that the RHGs are kept over time, and
that
for any
the variance estimator in (2.12) may be
written as
with
for
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