Mandallaz' model-assisted small area estimators
an S4 implementation of the unbiased extension of the
model-assisted' synthetic-regression estimator proposed by
Mandallaz (2013), Mandallaz et al. (2013) and Mandallaz (2014).
It yields smaller variances than the standard bias correction, the generalised regression estimator.
This package provides Mandallaz' extended synthetic-regression estimator for two- and
three-phase sampling designs with or without clustering.
See vignette('maSAE', package = 'maSAE') and demo('maSAE', package = 'maSAE') for introductions,
"?maSAE::predict" for help on the main feature.
Model-assisted estimators use models to improve the efficiency (i.e. reduce prediction error compared to design-based estimators) but need not assume them to be correct as in the model-based approach, which is advantageous in official statistics.
Mandallaz, D. 2013 Design-based properties of some small-area estimators in forest inventory with two-phase sampling. Canadian Journal of Forest Research 43(5), pp. 441–449. doi: 10.1139/cjfr-2012-0381.
Mandallaz, and Breschan, J. and Hill, A. 2013 New regression estimators in forest inventories with two-phase sampling and partially exhaustive information: a design-based Monte Carlo approach with applications to small-area estimation. Canadian Journal of Forest Research 43(11), pp. 1023–1031. doi: 10.1139/cjfr-2013-0181.
Mandallaz, D. 2014 A three-phase sampling extension of the generalized regression estimator with partially exhaustive information. Canadian Journal of Forest Research 44(4), pp. 383–388. doi: 10.1139/cjfr-2013-0449.
There are a couple packages for model-based small area estimation, see
1 2 3
## Not run: vignette('maSAE', package = 'maSAE') ## Not run: demo('design', package = 'maSAE') ## Not run: demo('maSAE', package = 'maSAE')
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