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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

We discuss several tests for whether a given set of independent andidentically distributed (i.i.d.) draws does not come from a specifiedprobability density function. The most commonly used are Kolmogorov-Smirnovtests, particularly Kuiper's variant, which focus on discrepancies between thecumulative distribution function for the specified probability density and theempirical cumulative distribution function for the given set of i.i.d. draws.Unfortunately, variations in the probability density function often getsmoothed over in the cumulative distribution function, making it difficult todetect discrepancies in regions where the probability density is small incomparison with its values in surrounding regions. We discuss tests withoutthis deficiency, complementing the classical methods. The tests of the presentpaper are based on the plain fact that it is unlikely to draw a random numberwhose probability is small, provided that the draw is taken from the samedistribution used in calculating the probability (thus, if we draw a randomnumber whose probability is small, then we can be confident that we did notdraw the number from the same distribution used in calculating theprobability).

Statistical tests for whether a given set of independent, identically distributed draws does not come from a specified probability density