Results 171 to 180 of about 4,317 (261)

Methods for Prioritizing Causal Genes in Molecular Studies of Human Disease: The State of the Art

open access: yesGenetic Epidemiology, Volume 50, Issue 3, April 2026.
ABSTRACT In the last decade, genome‐wide association studies (GWAS) have identified tens of thousands of common variants associated with a wide array of complex traits and diseases. Integration of GWAS with molecular data has informed the development of statistical tools for causal gene discovery.
Karina Patasova   +4 more
wiley   +1 more source

Testing Hypotheses of Covariate Effects on Topics of Discourse

open access: yesStatistical Analysis and Data Mining: An ASA Data Science Journal, Volume 19, Issue 2, April 2026.
ABSTRACT We introduce an approach to topic modeling with document‐level covariates that remains tractable in the face of large text corpora. This is achieved by de‐emphasizing the role of parameter estimation in an underlying probabilistic model, assuming instead that the data come from a fixed but unknown distribution whose statistical functionals are
Gabriel Phelan, David A. Campbell
wiley   +1 more source

Race‐related research in economics

open access: yesEconomica, Volume 93, Issue 370, Page 403-438, April 2026.
Abstract Issues of racial justice and economic inequalities between racial and ethnic groups have risen to the top of public debate. Economists' ability to contribute to these debates is based on the body of race‐related research. We study the volume and content of race‐related research in economics.
Arun Advani   +4 more
wiley   +1 more source

Multi‐Task Learning for Airport Surface Surveillance: A Review

open access: yesExpert Systems, Volume 43, Issue 4, April 2026.
ABSTRACT The rapid growth of air transportation has surpassed the capabilities of traditional airport surveillance methods, such as visual observation and auxiliary equipment (e.g., ADS‐B, MLAT, radar), which struggle to provide all‐area, all‐weather situation awareness.
Daoyong Fu   +6 more
wiley   +1 more source

Financial Time Series Uncertainty: A Review of Probabilistic AI Applications

open access: yesJournal of Economic Surveys, Volume 40, Issue 2, Page 915-953, April 2026.
ABSTRACT Probabilistic machine learning models offer a distinct advantage over traditional deterministic approaches by quantifying both epistemic uncertainty (stemming from limited data or model knowledge) and aleatoric uncertainty (due to inherent randomness in the data), along with full distributional forecasts.
Sivert Eggen   +4 more
wiley   +1 more source

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