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

An analyst is given a training set consisting of regression datasets $D_j$ ofdifferent sizes, which are distributed according to some $G_j$,$j=1,\ldots,\cal J$, where the distributions $G_j$ are assumed to form a randomsample generated by some common source. In particular, the $D_j$'s have acommon set of covariates and they are all labeled. The training set is used bythe analyst for selection of subsets of covariates denoted by ${P}^*(n)$, whoserole is described next. The multi-task problem we consider is as follows: givena number of random labeled datasets (which may be in the training set or not)$D_{J_k}$ of size $n_k$, $k=1,\ldots,K$, estimate separately for each datasetthe regression coefficients on the subset of covariates ${P}^*(n_k)$ and thenpredict future dependent variables given their covariates. Naturally, a largesample size $n_k$ of $D_{J_k}$ allows a larger subset of covariates, and thedependence of the size of the selected covariate subsets on $n_k$ is needed inorder to achieve good prediction and avoid overfitting. Subset selection isnotoriously difficult and computationally demanding, and requires largesamples; using all the regression datasets in the training set together amountsto borrowing strength toward better selection under suitable assumptions.Furthermore, using common subsets for all regressions having a given samplesize standardizes and simplifies the data collection and avoids having toselect and use a different subset for each prediction task. Our approach isefficient when the relevant covariates for prediction are common to thedifferent regressions, while the models' coefficients may vary betweendifferent regressions.

Optimal selection of sample-size dependent common subsets of covariates for multi-task regression prediction