Results 61 to 70 of about 2,044,246 (250)
Prediction intervals in conditionally heteroscedastic time series with stochastic components. [PDF]
Differencing is a very popular stationary transformation for series with stochastic trends. Moreover, when the differenced series is heteroscedastic, authors commonly model it using an ARMA-GARCH model. The corresponding ARIMA-GARCH model is then used to
Ruiz, Esther +2 more
core
Massively Scaling Heteroscedastic Classifiers
, 2023 Heteroscedastic classifiers, which learn a multivariate Gaussian distribution over prediction logits, have been shown to perform well on image classification problems with hundreds to thousands of classes.
Berent, Jesse +5 more
core
Business Strategy and the Environment, EarlyView.ABSTRACT This paper examines the relationship between industrial robotics adoption and ecological capacity, measured by biocapacity, using panel data from 50 countries over the period 2000–2024. We investigate the transmission mechanisms, non‐linearities, spatial spillovers, and heterogeneity characterizing this relationship.
Brahim Bergougui +1 more
wiley +1 more source
Board Gender Diversity and Environmental Credit Risk in Banking: A Global Study of Bank Governance
Business Strategy and the Environment, EarlyView.ABSTRACT This study investigates the relationship between board gender diversity and environmental credit risk in the global banking sector. Using a panel dataset of 345 publicly listed banks from 75 countries over the period 2018–2022, we find that greater female representation on bank boards is significantly associated with lower environmental credit
Kenza Mouti +2 more
wiley +1 more source
Filtering and Statistical Properties of Unimodal Maps Perturbed by Heteroscedastic Noises [PDF]
We propose a theory of unimodal maps perturbed by an heteroscedastic Markov chain noise and experiencing another heteroscedastic noise due to uncertain observation.
Tanzi, Matteo +3 more
core +3 more sources
On a multivariate conditional heteroscedastic model
, 1997 Tsay (1987) developed the conditional heteroscedastic autoregressive moving-average model, which includes the conditional heteroscedastic autoregressive and random coefficient autoregressive models as special cases.
Wong, H, Li, WK
core +1 more source
Business Strategy and the Environment, EarlyView.ABSTRACT The United States (U.S.) faces challenges in achieving its ambitious net‐zero carbon emissions target by 2050, with current emissions having fallen by less than 1% in 2024. Despite an investment of $500 billion in low‐carbon resources while holding the second‐largest green technology patent portfolio globally, it is further imperative to ...
Md Zubair Ahmad +5 more
wiley +1 more source
Moment Matters: Mean and Variance Causal Graph Discovery from Heteroscedastic Observational Data
CoRRHeteroscedasticity -- where the variance of a variable changes with other variables -- is pervasive in real data, and elucidating why it arises from the perspective of statistical moments is crucial in scientific knowledge discovery and decision-making. However, standard causal discovery does not reveal which causes act on the mean versus the variance,
openaire +2 more sources
Rapid and accurate flood extent mapping from Remote Sensing data, such as Synthetic Aperture Radar (SAR), is critical for operational disaster response, but standard Deep Learning models often produce physically impossible predictions due to a lack of hydrological constraints.
Gebre, Tewodros Syum +3 more
openaire +2 more sources
Corporate ESG Greenwashing: Does Regulatory Proximity Matter?
Business Strategy and the Environment, EarlyView.ABSTRACT Environmental, social, and governance (ESG) greenwashing undermines sustainable development, yet the influence of regulatory proximity on oversight is understudied. By introducing the “distance decay effect” from geoeconomics into ESG misconduct research and using a sample of Chinese listed firms from 2009 to 2022, this study reveals a ...
Weiqi Zhao +4 more
wiley +1 more source