Results 61 to 70 of about 9,896 (259)

Vine copula knockoffs for variable selection in gene expression studies

Canadian Journal of Statistics, EarlyView.
Abstract Identifying clinical and genetic markers is essential for stratifying cancer patients by survival outcomes and guiding personalized treatment strategies. However, gene expression studies often involve high‐dimensional predictors with mixed data types and complex dependence, which complicates reliable variable selection.
José Ulises Márquez Urbina, Alejandro Román Vásquez, Graciela González Farías, Gabriel Escarela   +3 more
wiley   +1 more source

Bayesian Robust Quantile Regression

, 2016
Traditional Bayesian quantile regression relies on the Asymmetric Laplace distribution (ALD) mainly because of its satisfactory empirical and theoretical performances. However, the ALD displays medium tails and it is not suitable for data characterized by strong deviations from the Gaussian hypothesis.
Bernardi, Mauro, Bottone, Marco, Petrella, Lea   +2 more
openaire   +2 more sources

The AI Sustainability Paradox: How Verification and Regulation Synergize to Curb Greenwashing in Emerging Markets

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT Artificial intelligence (AI) reflects a paradox for corporate sustainability: it provides tools for genuine socio‐economic improvement and enables greenwashing at scale. This study examines this duality in emerging Asian markets, where rapid AI adoption coincides with evolving regulatory regimes.
Ashutosh Yadav, Simplice A. Asongu
wiley   +1 more source

Bayesian Quantile Regression for Ordinal Models

Bayesian Analysis, 2016
The paper introduces a Bayesian estimation method for quantile regression in univariate ordinal models. Two algorithms are presented that utilize the latent variable inferential framework of Albert and Chib (1993) and the normal-exponential mixture representation of the asymmetric Laplace distribution.
openaire   +4 more sources

Graph Neural Network‐Based Prediction of Building Energy Consumption

Energy Science &Engineering, EarlyView.
A graph neural network that encodes a multi‐zone building as a graph accurately predicts hourly cooling and heating loads across three distinct climates, outperforming Random Forest and XGBoost baselines and serving as a fast surrogate to EnergyPlus simulations for scalable building energy management.
Ali Maboudi Reveshti, Jamal Dabbagh, Jhila Nasiri Reveshti, Karim Farajeyan Bonab, Farid Hosseini Mansoub   +4 more
wiley   +1 more source

Bayesian Estimation of Spatial Lagged Panel Quantile Regression Model

Applied Sciences
This paper proposes a Bayesian estimation method for spatial lagged panel quantile models. The proposed model simultaneously considers spatial lag effects of the dependent variable and the quantile regression framework, enabling effective capture of ...
Man Zhao, Rushan Huang, Hanfang Li, Youxi Luo, Qiming Liu   +4 more
doaj   +1 more source

Predicting EU Emissions Allowance Prices Using Macroeconomic Indicators and Hybrid AI Models

Journal of Forecasting, EarlyView.
ABSTRACT Predicting carbon allowance prices has grown more crucial in relation to carbon market regulation, financial strategy, and environmental policy development. This study examines a hybrid forecasting system that combines deep learning with ensemble machine learning models to forecast the price fluctuations of EU Emissions Allowance (EUAs) within
Saptarshi Ganguly, Pooja Sengupta, Divya Aggarwal   +2 more
wiley   +1 more source

Variational Bayesian Quantile Regression with Non-Ignorable Missing Response Data

Axioms
For non-ignorable missing response variables, the mechanism of whether the response variable is missing can be modeled through logistic regression. In Bayesian computation, the lack of a conjugate prior for the logistic function poses a significant ...
Juanjuan Zhang, Weixian Wang, Maozai Tian   +2 more
doaj   +1 more source

A New Implementation of Network GARCH Model for Stock Volatility and Co‐Volatility Forecasting

Journal of Forecasting, EarlyView.
ABSTRACT Volatility clustering and spillovers are key features of financial time series with many cross‐sectional assets. While network analysis links similar or correlated stocks and helps trace volatility spillovers, contemporary multivariate ARCH‐GARCH formulations struggle to represent structured network dependence and remain parsimonious.
Peiyi Zhou
wiley   +1 more source

Retirement Consumption Puzzle in Malaysia: Evidence from Bayesian Quantile Regression Model

Journal of Probability and Statistics, 2019
The objective of this study is to use the Bayesian quantile regression for studying the retirement consumption puzzle, which is defined as the drop in consumption upon retirement, using the cross-sectional data of the Malaysian Household Expenditure ...
Ros Idayuwati Alaudin, Noriszura Ismail, Zaidi Isa   +2 more
doaj   +1 more source