English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper focuses on drawing statistical inference based on a novel variantof maxima or minima nomination sampling (NS) designs. These sampling designsare useful for obtaining more representative sample units from the tails of thepopulation distribution using the available auxiliary ranking information.However, one common difficulty in performing NS in practice is that theresearcher cannot obtain a nominated sample unless he/she uniquely determinesthe sample unit with the highest or the lowest rank in each set. To overcomethis problem, a variant of NS which is called partial nomination sampling isproposed in which the researcher is allowed to declare that two or more unitsare tied in the ranks whenever he/she cannot find with high confidence thesample unit with the highest or the lowest rank with high confidence. Based onthis sampling design, two asymptotically unbiased estimators are developed forthe cumulative distribution function, which are obtained using maximumlikelihood and moment-based approaches, and their asymptotic normality isproved. Several numerical studies have shown that the developed estimators havehigher relative efficiencies than their counterpart in simple random samplingin analyzing either the upper or the lower tail of the parent distribution. Theprocedures that we are developed are then implemented on a real dataset fromthe Third National Health and Nutrition Examination Survey (NHANES III) toestimate the prevalence of osteoporosis among adult women aged 50 and over. Itis shown that in some certain circumstances, the techniques that we havedeveloped require only one-third of the sample size needed in SRS to achievethe desired precision. This results in a considerable reduction of time andcost compared to the standard SRS method.

Statistical Inference from Partially Nominated Sets: An Application to Estimating the Prevalence of Osteoporosis