In the following we describe an extension of the methodology
implemented in surveysd
. While the default rescaling method
described in vignette("Methodology")
follows the approach
of Preston, this vignette introduces an alternative rescaling method
based on the Rao-Wu bootstrap (Rao and Wu
1988).
Mathematical Formulation
More formally, the replicate weights are constructed as follows:
with
where:
-
is the design weight for PSU
in stratum
,
is the average design weight for the entire stratum
,
-
is the total number of units in stratum
,
-
is the number of PSUs in the original sample for stratum
,
-
is a resampling indicator that represents how many
times PSU
in stratum
was drawn in the bootstrap ,
-
is the number of units to be drawn in each replicate
for stratum
,
-
is the scaling factor used to adjust the weights during
the bootstrap process,
- and
is the sampling fraction in stratum
.
Use of Rao-Wu
The Rao-Wu bootstrap is particularly suited for complex survey designs with stratification and multi-stage selection. The design-based variance estimation benefits from a resampling method that accounts for the primary sampling stage and incorporates finite population corrections (FPC) directly in the replicate weights. The method is appropriate when the sampling fraction in the first stage (i.e., the share of selected PSUs within strata) is relatively small, typically below 10%.
Rao-Wu is not suitable when:
- The sampling
fraction in the first stage is large and the first-stage sampling is
without replacement.
- The survey does not include PSU-level
identifiers, making a first-stage resampling infeasible since the method
requires resampling entire PSUs to reflect first-stage sampling
variability.
- The design is single-stage, where methods like the
Preston bootstrap may be more appropriate.
In those cases, it is recommended to fall back on alternative
bootstrap methods such as the default Preston approach implemented in
surveysd
, which offers more flexibility for a wider range
of designs without PSU information.
Single PSUs
When dealing with multistage sampling designs, the issue of single PSUs, e.g. a single response unit at a stage or in a stratum, can arise. When applying resampling methods such as the Rao-Wu bootstrap, these single PSUs can introduce challenges in the resampling process. In the Rao-Wu method, we adjust for the presence of single PSUs by combining them with the next smallest stratum or cluster before applying the resampling procedure. This ensures that the resampling reflects the structure of the original design while maintaining appropriate variance estimates for the total and other statistics of interest.
Example Implementation
Load Dataset
library(surveysd)
set.seed(1234)
eusilc <- demo.eusilc(n = 2, prettyNames = TRUE)
eusilc[1:5, .(year, povertyRisk, gender, pWeight)]
Draw bootstrap replicates
For the bootstrap select ‘method = “Rao-Wu”’. Otherwise the default “Preston” is used.
dat_boot_rw <- draw.bootstrap(eusilc,
method = "Rao-Wu",
REP = 10,
hid = "hid",
weights = "pWeight",
strata = "region",
period = "year")
Calibrate bootstrap replicates
Calibrate each sample according to the distribution of
gender
(on a personal level) and region
(on a
household level).
dat_boot_calib <- recalib(dat_boot_rw,
conP.var = "gender",
conH.var = "region",
epsP = 1e-2,
epsH = 2.5e-2,
verbose = FALSE)
dat_boot_calib[1:5, .(year, povertyRisk, gender, pWeight, w1, w2, w3, w4)]