Description Approximation of Joint-inclusion probabilities References

jipApprox: Approximate inclusion probabilities for survey sampling

Function `jip_approx`

provides a number of approximations of the
second-order inclusion probabilities that require only the first-order inclusion
probabilities. These approximations may be employed in unequal probability sampling
design with high entropy. A more flexible approximation may be obtained by using
function `jip_MonteCarlo`

, which estimates inclusion probabilities
through a Monte Carlo simulation.

The variance of the Horvitz-Thompson total estimator may be then estimated by
plugging the approximated joint probabilities into the Horvitz-Thompson or
Sen-Yates-Grundy variance estimator using function `HTvar`

.

Matei, A.; Tillé, Y., 2005. Evaluation of variance approximations and estimators in maximum entropy sampling with unequal probability and fixed sample size. Journal of Official Statistics 21 (4), 543-570.

Haziza, D.; Mecatti, F.; Rao, J.N.K. 2008. Evaluation of some approximate variance estimators under the Rao-Sampford unequal probability sampling design. Metron LXVI (1), 91-108.

Fattorini, L. 2006. Applying the Horvitz-Thompson criterion in complex designs: A computer-intensive perspective for estimating inclusion probabilities. Biometrika 93 (2), 269–278

rhobis/jipApprox documentation built on Dec. 28, 2018, 5:22 p.m.

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