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

In the era of big data, structural variation and heterogeneity of high-dimensional data bring a great challenge to statistical modelling and computation. In this paper, wepropose a high-dimensional continuous threshold model for such data and develop a regularized expectile framework with the non-convex SCAD penalty. Due to theexistence of threshold, we have to tackle a non-linear and non-differentiable optimization problem. Inspired by the linear reparameterization technique and LLA strategy,we propose an efficient algorithm to solve this problem. Theoretical analysis and simulation results show that compared with the classical grid-search method, ouralgorithm has considerable computational advantages. Besides, our proposed method also performs well in variable selection, parameter estimation, thresholdidentification and data heterogeneity detection. These results indicate that our proposed method is effective in analysing high-dimensional heterogeneous data.

An improved algorithm for high-dimensional continuous threshold expectile model with variance heterogeneity