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

In this paper, we develop a generalization of the Gaussian quasi score test(GQST) for composite binary hypothesis testing. The proposed test, calledmeasure transformed GQST (MT-GQST), is based on the score-function of themeasure transformed Gaussian quasi maximum likelihood estimator (MT-GQMLE) thatoperates by empirically fitting a Gaussian model to a transformed probabilitymeasure of the data. By judicious choice of the transform we show that, unlikethe GQST, the proposed MT-GQST involves higher-order statistical moments andcan gain resilience to outliers, leading to significant mitigation of the modelmismatch effect on the decision performance. A data-driven procedure foroptimal selection of the measure transformation parameters is developed thatminimizes the spectral norm of the empirical asymptotic error-covariance of theMT-GQMLE. This amounts to maximization of an empirical worst-case asymptoticlocal power at a fixed asymptotic size. The MT-GQST is applied to locationmismatch detection of a near-field point source in a simulation example thatillustrates its robustness to outliers.

Measure Transformed Quasi Score Test with Application to Location Mismatch Detection