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Inverse probability weighting — Wikipedia (en)

Source: Wikipedia (en)
Original: https://en.wikipedia.org/wiki/Inverse_probability_weighting
License (as archived): CC-BY-SA-4.0
Archived: 2026-10-07T19:02:27.276Z
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Inverse probability weighting — Wikipedia (en) — Wikipedia (en), https://en.wikipedia.org/wiki/Inverse_probability_weighting (CC-BY-SA-4.0); archived copy: https://metraipolis.com/documento/dc_acffbc24ccc4 (Metraipolis archive dc_acffbc24ccc4, sha256 61802b9bbfb401dc01173cc08d7c8e938a2dc13eaff0a195a8f1f4b0077c00f7)

BibTeX

@misc{dcacffbc24ccc4,
  title = {Inverse probability weighting — Wikipedia (en)},
  howpublished = {Wikipedia (en)},
  url = {https://en.wikipedia.org/wiki/Inverse_probability_weighting},
  note = {License CC-BY-SA-4.0; archived copy https://metraipolis.com/documento/dc_acffbc24ccc4; sha256 61802b9bbfb401dc01173cc08d7c8e938a2dc13eaff0a195a8f1f4b0077c00f7},
  year = {2026}
}

Archived text

Reproduced under CC-BY-SA-4.0. Attribution: Wikipedia (en), https://en.wikipedia.org/wiki/Inverse_probability_weighting. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Inverse probability weighting is a statistical technique for estimating quantities related to a population other than the one from which the data was collected. Study designs with a disparate sampling population and population of target inference (target population) are common in application. There may be prohibitive factors barring researchers from directly sampling from the target population such as cost, time, or ethical concerns. A solution to this problem is to use an alternate design strategy, e.g. stratified sampling. Weighting, when correctly applied, can potentially improve the efficiency and reduce the bias of unweighted estimators. One very early weighted estimator is the Horvitz–Thompson estimator of the mean. When the sampling probability is known, from which the sampling population is drawn from the target population, then the inverse of this probability is used to weight the observations. This approach has been generalized to many aspects of statistics under various frameworks. In particular, there are weighted likelihoods, weighted estimating equations, and weighted probability densities from which a majority of statistics are derived. These applications codified the theory of other statistics and estimators such as marginal structural models, the standardized mortality ratio, and the EM algorithm for coarsened or aggregate data. Inverse probability weighting is also used to account for missing data when subjects with missing data cannot be included in the primary analysis. With an estimate of the sampling probability, or the probability that the factor would be measured in another measurement, inverse probability weighting can be used to inflate the weight for subjects who are under-represented due to a large degree of missing data.

Tema degli esploratori: scienze della vita e salute (deposito sistematico; categoria «Epidemiology»).
Fonte: Wikipedia in inglese, voce «Inverse probability weighting» — https://en.wikipedia.org/wiki/Inverse_probability_weighting
Wikidata Q17058619: «statistical technique» (dati Wikidata in CC0 1.0) — https://www.wikidata.org/wiki/Q17058619
Autori: i contributori di Wikipedia (cronologia: https://en.wikipedia.org/wiki/Inverse_probability_weighting?action=history).
Licenza: Creative Commons Attribuzione-Condividi allo stesso modo 4.0 (CC BY-SA 4.0) — https://creativecommons.org/licenses/by-sa/4.0/. Il riuso richiede attribuzione e stessa licenza.
Estratto dell'introduzione non modificato nel contenuto; revisione 1375607272, consultata il 2026-10-07T18:23:05.787Z tramite l'API ufficiale di Wikipedia. Impronta SHA-256 dell'estratto: a5ce106bd34d2d26e9e903644753890620d3893ba93969df2f7956eea55eba30