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 proposes an information-based inference method for partiallyidentified parameters in incomplete models that is valid both when the model iscorrectly specified and when it is misspecified. Key features of the methodare: (i) it is based on minimizing a suitably defined Kullback-Leiblerinformation criterion that accounts for incompleteness of the model anddelivers a non-empty pseudo-true set; (ii) it is computationally tractable;(iii) its implementation is the same for both correctly and incorrectlyspecified models; (iv) it exploits all information provided by variation indiscrete and continuous covariates; (v) it relies on Rao's score statistic,which is shown to be asymptotically pivotal.

Information Based Inference in Models with Set-Valued Predictions and Misspecification