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

As novel data collection becomes increasingly common, traditional dimensionreduction and data visualization techniques are becoming inadequate to analyzethese complex data. A surrogate-assisted sufficient dimension reduction (SDR)method for regression with a general metric-valued response on Euclideanpredictors is proposed. The response objects are mapped to a real-valueddistance matrix using an appropriate metric and then projected onto a largesample of random unit vectors to obtain scalar-valued surrogate responses. Anensemble estimate of the subspaces for the regression of the surrogateresponses versus the predictor is used to estimate the original central space.Under this framework, classical SDR methods such as ordinary least squares andsliced inverse regression are extended. The surrogate-assisted method appliesto responses on compact metric spaces including but not limited to Euclidean,distributional, and functional. An extensive simulation experiment demonstratesthe superior performance of the proposed surrogate-assisted method on syntheticdata compared to existing competing methods where applicable. The analysis ofthe distributions and functional trajectories of county-level COVID-19transmission rates in the U.S. as a function of demographic characteristics isalso provided. The theoretical justifications are included as well.

Data visualization and dimension reduction for metric-valued response regression