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

In the context of multilevel longitudinal data, where sample units arecollected in clusters, an important aspect that should be accounted for is theunobserved heterogeneity between sample units and between clusters. For thisaim we propose an approach based on nested hidden (latent) Markov chains, whichare associated to every sample unit and to every cluster. The approach allowsus to account for the mentioned forms of unobserved heterogeneity in a dynamicfashion; it also allows us to account for the correlation which may arisebetween the responses provided by the units belonging to the same cluster.Given the complexity in computing the manifest distribution of these responsevariables, we make inference on the proposed model through a compositelikelihood function based on all the possible pairs of subjects within everycluster. The proposed approach is illustrated through an application to adataset concerning a sample of Italian workers in which a binary responsevariable for the worker receiving an illness benefit was repeatedly observed.

Nested hidden Markov chains for modeling dynamic unobserved heterogeneity in multilevel longitudinal data