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 Tools annual

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 monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Economic models produce moment inequalities, which can be used to form testsof the true parameters. Confidence sets (CS) of the true parameters are derivedby inverting these tests. However, they often lack analytical expressions,necessitating a grid search to obtain the CS numerically by retaining the gridpoints that pass the test. When the statistic is not asymptotically pivotal,constructing the critical value for each grid point in the parameter space addsto the computational burden. In this paper, we convert the computational issueinto a classification problem by using a support vector machine (SVM)classifier. Its decision function provides a faster and more systematic way ofdividing the parameter space into two regions: inside vs. outside of theconfidence set. We label those points in the CS as 1 and those outside as -1.Researchers can train the SVM classifier on a grid of manageable size and useit to determine whether points on denser grids are in the CS or not. Weestablish certain conditions for the grid so that there is a tuning that allowsus to asymptotically reproduce the test in the CS. This means that in thelimit, a point is classified as belonging to the confidence set if and only ifit is labeled as 1 by the SVM.

Efficient Computation of Confidence Sets Using Classification on Equidistributed Grids