Results 41 to 50 of about 2,303,766 (135)

Deep quantile regression for growth and maturation reaction norms

Methods in Ecology and Evolution, Volume 17, Issue 1, Page 111-124, January 2026.
Abstract Understanding species' growth and maturation responses to anthropogenic and environmental pressures is crucial for tracking demographic shifts, phenotypic change and ensuring population sustainability. Traditional regression methods often focus on modelling the conditional mean of life‐history traits, potentially overlooking heterogeneity in ...
Guankui Liu, Chunlin Liu, Runlong Sun, Yongjun Tian, Peng Sun   +4 more
wiley   +1 more source

Geoadditive expectile regression

, 2010
Quantile regression has emerged as one of the standard tools for regression analysis that enables a proper assessment of the complete conditional distribution of responses even in the presence of heteroscedastic errors.
Sobotka, Fabian, Kneib, Thomas
core   +1 more source

Measuring the Impact of Transition Risk on Financial Markets: A Joint VaR‐ES Approach

Journal of Forecasting, Volume 44, Issue 6, Page 1907-1945, September 2025.
ABSTRACT Based on a joint quantile and expected shortfall semiparametric methodology, we propose a novel approach to forecasting market risk conditioned to transition risk exposure. This method allows us to forecast two climate‐related financial risk measures called CoClimateVaR and CoClimateES, being jointly elicitable, that capture the dependence of ...
Laura Garcia‐Jorcano, Lidia Sanchis‐Marco   +1 more
wiley   +1 more source

Reverse mixed data sampling based on expectile regression for inflation at risk and economic growth modeling

Journal of King Saud University: Computer and Information Sciences
This study proposes a Reverse Mixed Data Sampling based on Expectile Regression (R-MIDAS-ER) framework for modeling Inflation at Risk and examining the role of economic growth in inflation risk dynamics.
Muhammad Sjahid Akbar, Wisnowan Hendy Saputra, Dedy Dwi Prastyo, Anton Abdulbasah Kamil   +3 more
doaj   +1 more source

Spatio-Functional Nadaraya–Watson Estimator of the Expectile Shortfall Regression

Axioms
The main aim of this paper is to consider a new risk metric that permits taking into account the spatial interactions of data. The considered risk metric explores the spatial tail-expectation of the data.
Mohammed B. Alamari, Fatimah A. Almulhim, Zoulikha Kaid, Ali Laksaci   +3 more
doaj   +1 more source

Quantile Gravity: Economic Integration Agreements, Least Traded Goods, and Less Developed Economies

Review of International Economics, Volume 33, Issue 4, Page 951-987, September 2025.
ABSTRACT Gravity‐equation estimates of the elasticity of trade with respect to bilateral trade costs – or of coefficient estimates of binary variables for the presence or absence of economic integration agreements (EIAs) – are central to determining quantitatively economic welfare impacts of trade‐policy liberalizations. Despite decades of study, trade
Jeffrey H. Bergstrand, Matthew W. Clance
wiley   +1 more source

Estimation and inference for multi-kink expectile regression with nonignorable dropout

Statistical Theory and Related Fields
In this paper, we consider parameter estimation, kink points testing and statistical inference for a longitudinal multi-kink expectile regression model with nonignorable dropout.
Dongyu Li, Lei Wang
doaj   +1 more source

Statistical Learning and Topkriging Improve Spatio‐Temporal Low‐Flow Estimation

Water Resources Research, Volume 61, Issue 4, April 2025.
Abstract This study evaluates the potential of a novel hierarchical space‐time model for predicting monthly low‐flow in ungauged basins. The model decomposes the monthly low‐flows into a mean field and a residual field, where the mean field represents the seasonal low‐flow regime plus a long‐term trend component.
J. Laimighofer, G. Laaha
wiley   +1 more source

A Comprehensive Review of Option Value in Energy Economics

IET Renewable Power Generation, Volume 19, Issue 1, January/December 2025.
ABSTRACT This review examines the economic concept of option value for the economic integration of renewables in power systems, considering energy markets' distinctive characteristics. Energy markets exhibit limited storability, price volatility, seasonality patterns, and network constraints that necessitate specialised valuation approaches beyond ...
Spyros Giannelos, Danny Pudjianto, Federica Bellizio, Dawei Qiu, Goran Strbac   +4 more
wiley   +1 more source

Implicit policy constraint for offline reinforcement learning

CAAI Transactions on Intelligence Technology, Volume 9, Issue 4, Page 973-981, August 2024.
Abstract Offline reinforcement learning (RL) aims to learn policies entirely from passively collected datasets, making it a data‐driven decision method. One of the main challenges in offline RL is the distribution shift problem, which causes the algorithm to visit out‐of‐distribution (OOD) samples.
Zhiyong Peng, Yadong Liu, Changlin Han, Zongtan Zhou   +3 more
wiley   +1 more source