Results 121 to 130 of about 9,950,038 (297)

An observation‐driven state‐space model for claims size modelling

Canadian Journal of Statistics, EarlyView.
Abstract State‐space models are popular in econometrics. Recently, these models have gained some popularity in the actuarial literature. The best known state‐space models are of the Kalman‐filter type. These are called parameter‐driven because the observations do not impact the state‐space dynamics.
Jae Youn Ahn, Himchan Jeong, Mario V. Wüthrich   +2 more
wiley   +1 more source

Federated Learning Optimization Method in Non-IID Scenarios [PDF]

Jisuanji gongcheng
Federated Learning(FL) can collaborate to train global models without compromising data privacy. Nonetheless, this collaborative training approach faces the challenge of Non-IID in the real world; slow model convergence and low accuracy.
Huawei SONG, Shengqi LI, Fangjie WAN, Yuping WEI
doaj   +1 more source

On subset least squares estimation and prediction in vector autoregressive models with exogenous variables

Canadian Journal of Statistics, EarlyView.
Abstract We establish the consistency and the asymptotic distribution of the least squares estimators of the coefficients of a subset vector autoregressive process with exogenous variables (VARX). Using a martingale central limit theorem, we derive the asymptotic normal distribution of the estimators. Diagnostic checking is discussed using kernel‐based
Pierre Duchesne, Thanh‐Liem Huynh, Mylène Oscar   +2 more
wiley   +1 more source

An Overview of Autonomous Connection Establishment Methods in Peer-to-Peer Deep Learning

IEEE Access
The exchange of model parameters between peers is critical in peer-to-peer deep learning. Historically, connections between agents were assigned randomly based on network topology.
Robert Sajina, Nikola Tankovic, Ivo Ipsic   +2 more
doaj   +1 more source

Performance Enhancement in Federated Learning by Reducing Class Imbalance of Non-IID Data. [PDF]

Sensors (Basel), 2023
Seol M, Kim T.
europepmc   +1 more source

Consumer Choice of Modularized Products: A Conjoint Choice Experiment Approach [PDF]

Recent increases in flexibility and automation in the production of goods and services allow a growing number of suppliers to offer their products in flexible sets of modules from which consumers can create their own individualized packages.
Borgers, A.W.J., Dellaert, B.G.C., Louviere, J., Timmermans, H.J.P.   +3 more
core   +1 more source

Optimal dividends for a NatCat insurer in the presence of a climate tipping point

Canadian Journal of Statistics, EarlyView.
Abstract We study optimal dividend strategies for an insurance company facing natural catastrophe claims, anticipating the arrival of a climate tipping point after which the claim intensity and/or the claim size distribution of the underlying risks deteriorates irreversibly.
Hansjörg Albrecher, Pablo Azcue, Nora Muler   +2 more
wiley   +1 more source

Enhancing federated learning for IoT-based anomaly detection: A reputation-based client selection approach

Alexandria Engineering Journal
Federated Learning (FL) enables collaborative model training across decentralized, privacy-sensitive environments but often suffers from slow convergence, unbalanced client selection, and non‑IID data challenges.
Maha Jawad Alfadhil, Ali Baydoun, Moutaz Alazab, Hafeez Ur Rehman, Jihad Al Jaam, Sumaya Ali S.A. Al-Maadeed   +5 more
doaj   +1 more source

Asymptotic independence in more than two dimensions and its implications on risk management

Canadian Journal of Statistics, EarlyView.
Abstract In extreme value theory, the presence of asymptotic independence signifies that joint extreme events across multiple variables are unlikely. Although well understood in a bivariate context, the concept remains relatively unexplored when addressing the nuances of simultaneous occurrence of extremes in higher dimensions.
Bikramjit Das, Vicky Fasen‐Hartmann
wiley   +1 more source

Personalized Federated Learning Algorithm with Adaptive Clustering for Non-IID IoT Data Incorporating Multi-Task Learning and Neural Network Model Characteristics. [PDF]

Sensors (Basel), 2023
Hsu HY, Keoy KH, Chen JR, Chao HC, Lai CF.   +4 more
europepmc   +1 more source