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In jipApprox: Approximate Inclusion Probabilities for Survey Sampling

jipApprox: Approximate inclusion probabilities for survey sampling

Description

Approximate joint-inclusion probabilities in Unequal Probability Sampling, or compute Monte Carlo approximations of the first and second-order inclusion probabilities of a general sampling design as in Fattorini (2006) <doi:10.1093/biomet/93.2.269>.

Approximation of Joint-inclusion probabilities

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.

Author(s)

Maintainer: Roberto Sichera [e-mail]

References

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

See Also

Useful links:

• Report bugs at https://github.com/rhobis/jipApprox/issues

jipApprox documentation built on Aug. 26, 2023, 9:06 a.m.