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/

Zendy Plus

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

Zendy Tools

Zendy Open

Split sample methods have recently been put forward as a way to reduce thecoverage oscillations that haunt confidence intervals for parameters of latticedistributions, such as the binomial and Poisson distributions. We study splitsample intervals in the binomial setting, showing that these intervals can beviewed as being based on adding discrete random noise to the data. It is shownthat they can be improved upon by using noise with a continuous distributioninstead, regardless of whether the randomization is determined by the data oran external source of randomness. We compare split sample intervals to therandomized Stevens interval, which removes the coverage oscillationscompletely, and find the latter interval to have several advantages.

On split sample and randomized confidence intervals for binomial proportions