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

Longitudinal data are commonly encountered in many fields. Many statistical models have been developed, among which the time-varying coefficient model has been shown to be effective for many practical problems. Long-tailed/contaminated distributions are not uncommon and cannot be accommodated using non-robust likelihood-based estimation. Another common limitation shared by many of the existing methods is the insufficient account for the interconnections among covariates. In this study, we adopt a least absolute deviation loss function to achieve robustness. For the selection of relevant covariates, a penalization approach is adopted. Significantly advancing from the existing literature, we describe the interconnections among covariates using a network structure and develop novel penalties to accommodate the network connectivity and connection measures. Consistency properties are rigorously established. Numerical studies, including both simulations and data analysis, demonstrate the competitive practical performance of the proposed method.

Network-adaptive robust penalized estimation of time-varying coefficient models with longitudinal data