Results 111 to 120 of about 46,274,052 (301)
Mutual Adaptation and Influence: Review of Latent Dynamics Models in Human–Robot Interaction
Advanced Robotics Research, EarlyView.Robots are becoming better teammates by learning hidden patterns in how people act. This review covers how state‐of‐the‐art approaches leverage these patterns to help robots not only react but also anticipate and guide cooperation. Here, we synthesize a unifying framework for these approaches, classify and review existing works, and highlight key ...
Mason O. Smith +4 more
wiley +1 more source
Crisis, Value at Risk, and Conditional Extreme Value Theory via Garch-Jump Model
, 2010 This study develops a new conditional extreme value theory-based model (EVT) combined with the GARCH-Jump model to forecast extreme risks. This paper utilizes the GARCH-Jump model to asymmetrically feed back the past realization of jump innovation to the
Ze-To, S.
core
Robust conditional variance estimation and value-at-risk
The Journal of Risk, 2001 A common approach to estimating the conditional volatility of short horizon asset returns is to use an exponentially weighted moving average (EWMA) of squared past returns. The EWMA estimator is based on the maximum likelihood estimator of the variance of the normal distribution, and is thus optimal when returns are conditionally normal. However, there
Richard D. F. Harris, Cherif Guermat
openaire +2 more sources
Advanced Robotics Research, EarlyView.DRIVE‐SAFE evaluates learning‐based, black‐box autonomous driving policies against evolving temporal safety requirements using Signal Temporal Logic robustness metrics. It aggregates distributional robustness measures with domain‐informed weights to guide iterative retraining.
Kristy Sakano +3 more
wiley +1 more source
On a Multivariate Extension for Copula-Based Conditional Value at Risk
Journal of Statistical Theory and Applications (JSTA)Copula-based Conditional Value at Risk ( $$\textrm{CCVaR}$$ ) is a real-valued tail risk measure for multivariate random vectors defined through conditioning on a copula level set.
Andres Mauricio Molina Barreto
doaj +1 more source
Advanced Robotics Research, EarlyView.This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao +6 more
wiley +1 more source
Advanced Robotics Research, EarlyView.This review brings together 20 publications describing 11 robotic system families developed for instrument handling, sterile‐package opening, supply delivery, and operating room logistics. Perception is becoming more capable through deep learning and language‐guided control, but autonomy remains limited, evidence is largely laboratory‐based, and no ...
Eugenia Mawuenya Akpo +4 more
wiley +1 more source
BarekengThis study aims to estimate Conditional Value at Risk (CVaR) for insurance company stock portfolios using a machine learning approach to improve the accuracy of financial risk measurement under extreme market conditions.
Purwanto Purwanto, Agna Olivia
doaj +1 more source
Advanced Science, EarlyView.The adipocyte MKK3–p38β axis emerges as an adaptive stress‐response pathway during obesity. Human visceral adipose tissue analyses, adipocyte‐specific mouse models, and primary adipocytes show that the MKK3–p38β axis supports insulin responsiveness by restraining p70S6K‐dependent inhibitory IRS1 feedback and preserving AKT–GLUT4 signaling. This pathway
Edgar Bernardo +20 more
wiley +1 more source
Comparing univariate and multivariate models to forecast portfolio value-at-risk [PDF]
This article addresses the problem of forecasting portfolio value-at-risk (VaR) with multivariate GARCH models vis-à-vis univariate models. Existing literature has tried to answer this question by analyzing only small portfolios and using a testing ...
Andre A. P. +2 more
core