Robust confidence intervals for a proportion using ranked-set sampling

Autor:	Yimin Zhang, Jesse Frey
Rok vydání:	2021
Předmět:	Statistics and Probability RSS Statistics Coverage probability Population proportion Sampling (statistics) computer.file_format Interval (mathematics) Bayesian inference computer Robust confidence intervals Confidence interval Mathematics
Zdroj:	Journal of the Korean Statistical Society. 50:1009-1028
ISSN:	2005-2863 1226-3192
DOI:	10.1007/s42952-020-00103-3
Popis:	We develop two new approximate confidence interval methods for estimating a population proportion using balanced ranked-set sampling (RSS). Unlike existing RSS-based methods, the new methods control the coverage probability well not just under perfect rankings, but also under imperfect rankings. One method uses a Wilson-type interval, and the other is based on making a mid-P adjustment to a Clopper–Pearson-type interval. Both methods rely on a new maximum-likelihood-based method for estimating the proportions in the judgment strata when the overall proportion is given, and both can be computed even for large sample sizes.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::56f54e2e7d8150139665334682c060bc https://doi.org/10.1007/s42952-020-00103-3 Zobrazit plný text záznamu Full text from SpringerLink