Results 41 to 50 of about 169 (137)

Overall Organizational Justice Trajectories Among Newcomers: How Do Justice Perceptions Develop and Why Does It Matter?

open access: yesBusiness Ethics, the Environment &Responsibility, EarlyView.
ABSTRACT While organizational justice perceptions are often thought to be stable, empirical evidence highlights substantial within‐person fluctuations over time. The development of these justice fluctuations may have important implications for newcomers' enactment of organizational citizenship behaviors (OCB).
Constanze Eib   +4 more
wiley   +1 more source

ReMoDe – Recursive modality detection in distributions of ordinal data

open access: yesBritish Journal of Mathematical and Statistical Psychology, EarlyView.
Abstract The detection of the number of modes in distributions of ordinal data is relevant for applied researchers across disciplines, from uncovering polarization to detecting incidence groups in clinical symptom scales. Yet, established modality detection methods are either purely descriptive or not developed for ordinal data.
Madlen Hoffstadt   +3 more
wiley   +1 more source

The Distributional Effects of Economic Uncertainty*

open access: yesInternational Economic Review, EarlyView.
ABSTRACT We study the distributional implications of uncertainty shocks by developing a model that links macroeconomic aggregates to the US distribution of earnings and consumption. Our findings suggest that the fraction of low‐earning workers decreases initially, while the share of households reporting low consumption increases.
Florian Huber   +2 more
wiley   +1 more source

Estimating Velocities of Infectious Disease Spread Through Spatio‐Temporal Log‐Gaussian Cox Point Processes

open access: yesInternational Statistical Review, EarlyView.
Summary Understanding the spread of infectious diseases such as COVID‐19 is crucial for informed decision‐making and resource allocation. A critical component of disease behaviour is the velocity with which disease spreads, defined as the rate of change between time and space.
Fernando Rodriguez Avellaneda   +2 more
wiley   +1 more source

A Bayesian approach to spectroscopic depth sectioning for locating dopant atoms

open access: yesJournal of Microscopy, EarlyView.
Abstract Locating dopants in 3D is of great interest as advanced materials and devices increasingly rely on control at atomic dimensions, and microscopy tools are constantly in development for this purpose. One potential tool is electron energy loss spectroscopy (EELS) depth sectioning, where a core‐loss signal is collected as a function of electron ...
Michael Deimetry   +3 more
wiley   +1 more source

Reporting Guidelines for Meta‐Analysis in Economics—Updated for AI

open access: yesJournal of Economic Surveys, EarlyView.
ABSTRACT Meta‐analysis is how science takes stock of its vast research output. The advent of increasingly capable artificial intelligence (AI) promises an unprecedented ability to identify and synthesize relevant research and its findings. In this document, the meta‐analysis of economics research network (MAER‐Net) updates existing Reporting Guidelines
Nikolai Cook   +12 more
wiley   +1 more source

Sparse Causal Dynamic Linear Regression

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT We develop a sparse causal dynamic regression framework for long multivariate time series. With very long time series, the potentially large number of lags and leads in a dynamic regression model often makes time‐domain estimation numerically unstable or intractable.
Rui Huang, Kung‐Sik Chan
wiley   +1 more source

Methods for Uncertainty Quantification in Dictionary Matching to Advance Reliability of Quantitative MRI

open access: yesMagnetic Resonance in Medicine, Volume 96, Issue 4, Page 1956-1971, October 2026.
ABSTRACT Aims Purpose: Dictionary matching is a standard tool in quantitative MRI (qMRI), but typically lacks uncertainty quantification (UQ). This is critical when advanced reconstructions (e.g., compressed sensing, deep learning) introduce complex‐valued, spatially varying, and temporally correlated noise that violates standard assumptions of ...
Brian Toner   +7 more
wiley   +1 more source

Deep Spatially Varying Coefficient Model for Interpolation of Non‐Stationary Meteorological Data

open access: yesEnvironmetrics, Volume 37, Issue 6, September 2026.
ABSTRACT In spatial statistics, the spatially varying coefficient model (SVCM) is widely applied in the analysis and interpolation of non‐stationary spatial data. By incorporating spatially varying coefficients, the model can capture spatial heterogeneity and provide an attractive interpretation of response‐covariate associations.
Tong Wu, Nan Chen, Zhi‐Sheng Ye
wiley   +1 more source

Genomic prediction in quinoa across contrasting environments using statistical and machine learning models

open access: yesThe Plant Genome, Volume 19, Issue 3, September 2026.
Abstract Quinoa (Chenopodium quinoa Willd.) is gaining global importance for its nutritional value and adaptability; however, breeding progress remains limited. Genomic selection (GS), combined with rapid generation cycles, offers a strategy to accelerate genetic improvement.
Clara S. Stanschewski   +8 more
wiley   +1 more source

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