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 consider regression problems subject to arbitrary noise inthe operator or design matrix. This characterization appropriately models manyphysical phenomena with uncertainty in the regressors. Although the problem hasbeen studied extensively for ordinary/total least squares, and via models thatimplicitly or explicitly assume Gaussianity, less attention has been paid toimproving estimation for regression problems under general uncertainty in thedesign matrix. To address difficulties encountered when dealing withdistributions of sums of random variables, we rely on the saddle point methodto estimate densities and form an approximate log-likelihood to maximize. Weshow that the proposed method performs favorably against other classicalmethods.

Approximate maximum likelihood estimators for linear regression with design matrix uncertainty