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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

A prediction interval covers a future observation from a random process inrepeated sampling, and is typically constructed by identifying a pivotalquantity that is also an ancillary statistic. Analogously, a tolerance intervalcovers a population percentile in repeated sampling and is often based on apivotal quantity. One approach we consider in non-normal models leverages alink function resulting in a pivotal quantity that is approximately normallydistributed. In settings where this normal approximation does not hold weconsider a second approach for tolerance and prediction based on a confidenceinterval for the mean. These methods are intuitive, simple to implement, haveproper operating characteristics, and are computationally efficient compared toBayesian, re-sampling, and machine learning methods. This is demonstrated inthe context of multi-site clinical trial recruitment with staggered siteinitiation, real-world time on treatment, and end-of-study success for aclinical endpoint.

Tolerance and Prediction Intervals for Non-normal Models