Results 51 to 60 of about 2,303,766 (135)

How data heterogeneity affects innovating knowledge and information in gene identification: A statistical learning perspective

Journal of Innovation & Knowledge
Data heterogeneity, particularly noted in fields such as genetics, has been identified as a key feature of big data, posing significant challenges to innovation in knowledge and information.
Jun Zhao, Fangyi Lao, Guan'ao Yan, Yi Zhang   +3 more
doaj   +1 more source

Next Generation Public Supply Water Withdrawal Estimation for the Conterminous United States Using Machine Learning and Operational Frameworks

Water Resources Research, Volume 60, Issue 7, July 2024.
Abstract Estimation of human water withdrawals is more important now than ever due to uncertain water supplies, population growth, and climate change. Fourteen percent of the total water withdrawal in the United States is used for public supply, typically including deliveries to domestic, commercial, and occasionally including industrial, irrigation ...
Ayman Alzraiee, Richard Niswonger, Carol Luukkonen, Josh Larsen, Donald Martin, Deidre Herbert, Cheryl Buchwald, Cheryl Dieter, Lisa Miller, Jana Stewart, Natalie Houston, Scott Paulinski, Kristen Valseth   +12 more
wiley   +1 more source

Large-dimensional Expectile Regression with Heavy-tailed Data [PDF]

, 2023
High-dimensional data can often display heterogeneity due to heteroscedastic variance or inhomogeneous covariate effects. Penalized quantile and expectile regression methods offer useful tools to detect heteroscedasticity in high-dimensional data ...
Wang, Zian
core   +1 more source

A Bayesian realized threshold measurement GARCH framework for financial tail risk forecasting

Journal of Forecasting, Volume 43, Issue 1, Page 40-57, January 2024.
Abstract This paper proposes an innovative threshold measurement equation to be employed in a Realized‐Generalized Autoregressive Conditional Heteroskedastic (GARCH) framework. The proposed framework incorporates a nonlinear threshold regression specification to consider the leverage effect and model the contemporaneous dependence between the observed ...
Chao Wang, Richard Gerlach
wiley   +1 more source

Flexible Expectile Regression in Reproducing Kernel Hilbert Spaces

, 2017
Expectile, first introduced by Newey and Powell in 1987 in the econometrics literature, has recently become increasingly popular in risk management and capital allocation for financial institutions due to its desirable properties such as coherence and ...
Hui Zou, Yi Yang, Zhang, Teng, Zou, Hui, Teng Zhang, Yang, Yi   +5 more
core   +1 more source

Retire: Robust Expectile Regression in High Dimensions [PDF]

, 2023
High-dimensional data can often display heterogeneity due to heteroscedastic variance or inhomogeneous covariate effects. Penalized quantile and expectile regression methods offer useful tools to detect heteroscedasticity in high-dimensional data.
Tan, Kean Ming, Man, Rebeka, Zhou, Wen-Xin, Wang, Zian   +3 more
core   +1 more source

Multifunctional Expectile Regression Estimation in Volterra Time Series: Application to Financial Risk Management

Axioms
We aim to analyze the dynamics of multiple financial assets with variable volatility. Instead of a standard analysis based on the Black–Scholes model, we proceed with the multidimensional Volterra model, which allows us to treat volatility as a ...
Somayah Hussain Alkhaldi, Fatimah Alshahrani, Mohammed Kbiri Alaoui, Ali Laksaci, Mustapha Rachdi   +4 more
doaj   +1 more source

An analysis of life expectancy and economic production using expectile frontier zones [PDF]

The wealth of a country is assumed to have a strong non-linear influence on the life expectancy of its inhabitants. We follow up on research by Preston and study the relationship with gross domestic product.
Sabine K. Schnabel, Paul Eilers
core  

From Halfspace M-Depth to Multiple-output Expectile Regression [PDF]

, 2019
Despite the importance of expectiles in fields such as econometrics, risk management, and extreme value theory, expectile regression—or, more generally, M-quantile regression—unfortunately remains limited to single-output problems.
Daouia, Abdelaati, Paindaveine, Davy
core   +1 more source

Nonparametric Estimation of Dynamic Value-at-Risk: Multifunctional GARCH Model Case

Mathematics
Value-at-Risk (VaR) estimation using the GARCH model is an important topic in financial data analysis. It allows for an increase in the accuracy of risk assessment by controlling time-varying volatility.
Zouaoui Chikr-Elmezouar, Ali Laksaci, Ibrahim M. Almanjahie, Fatimah Alshahrani   +3 more
doaj   +1 more source