Results 51 to 60 of about 1,032,480 (246)
Robust Estimation of Causal Heteroscedastic Noise Models
, 2023 Distinguishing the cause and effect from bivariate observational data is the foundational problem that finds applications in many scientific disciplines.
Duong, Bao +3 more
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Business Strategy and the Environment, EarlyView.ABSTRACT This study theorises and empirically tests performative purpose alignment theory (PPAT), which conceptualises corporate purpose as a performative artefact materialised through discursive and multimodal signals. To operationalise this, we introduced the SDG–Purpose Alignment Index (SPAI), a computational construct that quantifies the thematic ...
Augustine Okeke, Ifeanyi Ugbebor
wiley +1 more source
Retrieval of aboveground crop nitrogen content with a hybrid machine learning method
International Journal of Applied Earth Observations and Geoinformation, 2020 Hyperspectral acquisitions have proven to be the most informative Earth observation data source for the estimation of nitrogen (N) content, which is the main limiting nutrient for plant growth and thus agricultural production.
Katja Berger +6 more
doaj +1 more source
Canadian Journal of Statistics, EarlyView.Abstract Irregular observation times are common in longitudinal observational studies and can affect causal inferences. We use data from electronic health records of Kaiser Permanente Washington patients in the United States who initiated an antidepressant medication between 2008 and 2018, with a confirming diagnosis of depression. We are interested in
Janie Coulombe +2 more
wiley +1 more source
LSSVR Model of G-L Mixed Noise-Characteristic with Its Applications
Entropy, 2020 Due to the complexity of wind speed, it has been reported that mixed-noise models, constituted by multiple noise distributions, perform better than single-noise models.
Shiguang Zhang +4 more
doaj +1 more source
Cross-Domain Few-Shot Learning Between Different Imaging Modals for Fine-Grained Target Recognition
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Fine-grained target recognition in synthetic aperture radar (SAR) or infrared imaging modal is an open problem in some application scenarios where training samples are scarce.
Yuan Tai +3 more
doaj +1 more source
Enhancing generalizability theory with mixed-effects models for heteroscedasticity in psychological measurement: A theoretical introduction with an application from EEG data. [PDF]
Br J Math Stat PsycholAbstract Generalizability theory (G‐theory) defines a statistical framework for assessing measurement reliability by decomposing observed variance into meaningful components attributable to persons, facets, and error. Classic G‐theory assumes homoscedastic residual variances across measurement conditions, an assumption that is often violated in ...
Rast P, Clayson PE.
europepmc +2 more sources
Corporate Social Responsibility and Environmental Management, EarlyView.ABSTRACT This study investigates how internal governance design supports credible ESG performance by distinguishing between Incentive and Oversight Architectures. Using 13,993 firm‐year observations of US nonfinancial firms from 2018 to 2024, we estimate fixed effects and two‐step system GMM models.
Beyza Gürel +2 more
wiley +1 more source
Comparison of Semiparametric Models in the Presence of Noise and Outliers
Journal of Applied MathematicsVarious studies have examined generalized additive models (GAMs), comparing thin plate splines (tp), P-splines (ps), cubic regression splines (cr), and Gaussian processes (gp) for discrete choice data, function approximation, and in the presence of ...
Daniel Edinam Wormenor +2 more
doaj +1 more source
Research and Review Journal of Nondestructive TestingOne main challenge in structural health monitoring is distinguishing between the effects of actual system changes and changing environmental conditions (EC) on the monitored parameters. For this, data normalisation can be performed.
Britt Kahrger +4 more
doaj +1 more source