Results 11 to 20 of about 2,303,766 (135)
Reproducing kernel‐based functional linear expectile regression [PDF]
, 2021 Expectile regression is a useful alternative to conditional mean and quantile regression for characterizing a conditional response distribution, especially when the distribution is asymmetric or when its tails are of interest. In this article, we propose
Linglong Kong (4532590) +5 more
core +7 more sources
Composite Expectile Regression with Gene-environment Interaction [PDF]
, 2022 If error distribution has heteroscedasticity, it voliates the assumption of linear regression. Expectile regression is a powerful tool for estimating the conditional expectiles of a response variable in this setting.
Lin, Jinghang, Huang, Yuan, Ma, Shuangge
core +1 more source
Efficient Distributed Learning for Large-Scale Expectile Regression With Sparsity
IEEE Access, 2021 High-dimensional datasets often display heterogeneity due to heteroskedasticity or other forms of non-location-scale covariance effects. When the size of datasets becomes very large, it may be infeasible to store all of the high-dimensional datasets on ...
Yingli Pan, Zhan Liu
doaj +1 more source
IEEE Access, 2022 The prediction of mechanical properties of hot rolled strips can be used for on-line dynamic control of product properties and optimal design of new steel grade.
Xiaoxia He +3 more
doaj +1 more source
Biometrical Journal, Volume 65, Issue 8, December 2023., 2023 Abstract In this work, we intersect data on size‐selected particulate matter (PM) with vehicular traffic counts and a comprehensive set of meteorological covariates to study the effect of traffic on air quality. To this end, we develop an M‐quantile regression model with Lasso and Elastic Net penalizations.
M. Giovanna Ranalli +3 more
wiley +1 more source
Multivariate Expectiles, Expectile Depth and Multiple-Output Expectile Regression
, 2020 Despite the importance of expectiles in fields such as econometrics, risk management, and extreme value theory, expectile regression unfortunately remains limited to single-output problems.
Daouia, Abdelaati, Paindaveine, Davy
core +3 more sources
Large-Scale Expectile Regression With Covariates Missing at Random
IEEE Access, 2020 Analysis of large volumes of data is very complex due to not only a high level of skewness and heteroscedasticity of variance but also the phenomenon of missing data.
Yingli Pan, Zhan Liu, Wen Cai
doaj +1 more source
Modeling uncertainty in financial tail risk: A forecast combination and weighted quantile approach
Journal of Forecasting, Volume 42, Issue 7, Page 1648-1663, November 2023., 2023 Abstract A novel forecast combination and weighted quantile‐based tail risk forecasting framework is proposed, aiming to reduce the impact of modeling uncertainty. The proposed approach is based on a two‐step estimation procedure. The first step involves the combination of value‐at‐risk (VaR) forecasts at a grid of quantile levels.
Giuseppe Storti, Chao Wang
wiley +1 more source
GHG Global Emission Prediction of Synthetic N Fertilizers Using Expectile Regression Techniques
Atmosphere, 2023 Agriculture accounts for a large percentage of nitrous oxide (N2O) emissions, mainly due to the misapplication of nitrogen-based fertilizers, leading to an increase in the greenhouse gas (GHG) footprint.
Kaoutar Benghzial +5 more
doaj +1 more source
Less disagreement, better forecasts: Adjusted risk measures in the energy futures market
Journal of Futures Markets, Volume 43, Issue 10, Page 1332-1372, October 2023., 2023 Abstract This paper develops a generic adjustment framework to improve in the market risk forecasts of diverse risk forecasting models, which indicates the degree to which risk is under‐ and overestimated. In the context of the energy commodity market, a market in which tail risk management is of crucial importance, the empirical analysis shows that ...
Ning Zhang, Yujing Gong, Xiaohan Xue
wiley +1 more source