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Using an Approximate Bayesian Bootstrap to Multiply Impute Nonignorable Missing Data
An Approximate Bayesian Bootstrap (ABB) offers advantages in incorporating appropriate uncertainty when imputing missing data, but most implementations of the ABB have lacked the ability to handle nonignorable missing data where the probability of missingness depends on unobserved values. This paper...
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| Huvudupphovsmän: | , |
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| Materialtyp: | Artigo |
| Språk: | Inglês |
| Publicerad: |
2008
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| Ämnen: | |
| Länkar: | https://ncbi.nlm.nih.gov/pmc/articles/PMC2678725/ https://ncbi.nlm.nih.gov/pubmed/20016665 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.csda.2008.07.042 |
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