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

This paper derives the nonparametric maximum likelihood estimator (NPMLE) ofa distribution function from observations which are subject to both bias andcensoring. The NPMLE is obtained by a simple EM algorithm which is an extensionof the algorithm suggested by Vardi (Biometrika, 1989) for size biased data.Application of the algorithm to many models is discussed and a simulation studycompares the estimator's performance to that of the product-limit estimator(PLE). An example demonstrates the utility of the NPMLE to data where the PLEis inappropriate.

Nonparametric estimation of a distribution function under biased sampling and censoring