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

This paper introduces a new property of estimators of the strength ofstatistical association, which helps characterize how well an estimator willperform in scenarios where dependencies between continuous and discrete randomvariables need to be rank ordered. The new property, termed the estimatorresponse curve, is easily computable and provides a marginal distributionagnostic way to assess an estimator's performance. It overcomes notabledrawbacks of current metrics of assessment, including statistical power, bias,and consistency. We utilize the estimator response curve to test variousmeasures of the strength of association that satisfy the data processinginequality (DPI), and show that the CIM estimator's performance comparesfavorably to kNN, vME, AP, and H_{MI} estimators of mutual information. Theestimators which were identified to be suboptimal, according to the estimatorresponse curve, perform worse than the more optimal estimators when tested withreal-world data from four different areas of science, all with varyingdimensionalities and sizes.

On the Effect of Suboptimal Estimation of Mutual Information in Feature Selection and Classification