Results 131 to 140 of about 9,950,038 (297)
IEEE Transactions on Knowledge and Data EngineeringFederated learning (FL) has attracted increasing attention in recent years due to its data privacy preservation and great applicability to large-scale user scenarios. However, when FL faces numerous clients, it is inevitable to emerge the non-independent
Lin Chen +4 more
semanticscholar +1 more source
Bayesian clustering of multivariate extremes
Canadian Journal of Statistics, EarlyView.Abstract The asymptotic dependence structure between multivariate extreme values is fully characterized by their projections on the unit simplex. Under mild conditions, the only constraint on the resulting distributions is that their marginal means must be equal, which results in a nonparametric model that can be difficult to use in applications ...
Sonia Alouini, Anthony C. Davison
wiley +1 more source
DAG-Based Blockchain Sharding for Secure Federated Learning with Non-IID Data. [PDF]
Sensors (Basel), 2022 Lee J, Kim W.
europepmc +1 more source
Waves of Uncertainty: Crude Oil Under Geopolitical, Economic, and ESG Turbulence
Energy Science &Engineering, EarlyView.Dynamic copula and wavelet coherence reveal that geopolitical, economic, and sustainability uncertainties significantly shape crude oil price co‐movements. Long‐term coherence, especially post‐2015, highlights the growing role of ESG risks alongside geopolitical shocks and economic crises in global energy risk transmission.
Sana Braiek +3 more
wiley +1 more source
Adversarially-Regularized Mixed Effects Deep Learning (ARMED) Models Improve Interpretability, Performance, and Generalization on Clustered (non-iid) Data. [PDF]
IEEE Trans Pattern Anal Mach Intell, 2023 Nguyen KP, Treacher AH, Montillo AA.
europepmc +1 more source
PFL-NON-IID Framework: Evaluating MOON Algorithm on Handling Non-IID Data Distributions
, 2023 Sheng Chen +3 more
openaire +1 more source
Mortality Forecasting Using Variational Inference
Journal of Forecasting, EarlyView.ABSTRACT This paper considers the problem of forecasting mortality rates. A large number of models have already been proposed for this task, but they generally have the disadvantage of either estimating the model in a two‐step process, possibly losing efficiency, or relying on methods that are cumbersome for the practitioner to use.
Patrik Andersson, Mathias Lindholm
wiley +1 more source
Converting 1-Day Volatility to h-Day Volatitlity: Scaling by Root-h is Worse Than You Think [PDF]
We show that the common practice of converting 1-day volatility estimates to h-day estimates by scaling by the sqaure root of h is inappropriate and produces overestimates of the variability of long-horizon volatility.
Andrew Hickman +3 more
core
A Fuzzy Framework for Realized Volatility Prediction: Empirical Evidence From Equity Markets
Journal of Forecasting, EarlyView.ABSTRACT This study introduces a realized volatility fuzzy time series (RV‐FTS) model that applies a fuzzy c‐means clustering algorithm to estimate time‐varying c$$ c $$ latent volatility states and their corresponding membership degrees. These memberships are used to construct a fuzzified volatility estimate as a weighted average of cluster centroids.
Shafqat Iqbal, Štefan Lyócsa
wiley +1 more source
Journal of Forecasting, EarlyView.ABSTRACT We study the accuracy of a variety of parametric price duration‐based realized variance estimators constructed via various financial duration models and compare their forecasting performance with the performance of various nonparametric return‐based realized variance estimators.
Björn Schulte‐Tillmann +2 more
wiley +1 more source