Results 21 to 30 of about 4,032,520 (72)

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 estimation and variables selection for binary composite quantile regression [PDF]

, 2020
In this paper, Bayesian hierarchical model proposed to estimate the coefficients of the composite quantile regression model when the response variable is binary.  For selecting variables in binary composite quantile regression lasso the adaptive ...
Flaih, Ahmad Naeem, Alhusseini, Fadel Hamid Hadi, Alshaybawee, Taha   +2 more
core   +1 more source

A note on generalised information criteria for structured sparse models

International Statistical Review, EarlyView.
Summary We propose a generalised information criteria ( gic) that accounts for sparsity pattern in the model. We obtain both asymptotic and nonasymptotic results for model selection. Moreover, we show that the gic is useful for selecting the regularisation parameter in regularised m$$ m $$ estimation in high‐dimensional scenarios.
Eduardo Fonseca Mendes, Gabriel Jardim Pereira Pinto   +1 more
wiley   +1 more source

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

Journal of Forecasting, Volume 45, Issue 8, Page 3403-3426, December 2026.
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

Key Drivers of Flood Damage Differ Between Low and High Water Depths

Journal of Flood Risk Management, Volume 19, Issue 4, December 2026.
ABSTRACT Understanding which factors have the greatest influence on flood damage helps decision‐makers to target interventions more effectively. This study investigates whether the explanatory variables for residential flood damage differ between low‐ and high‐water depth conditions.
M. P. Ávila‐Guzmán, D. Rodriguez Castro, T. Endendijk, L. Dillenardt, R. K. Guntu, S. Erpicum, A. H. Thieken, J. C. J. H. Aerts, H. Kreibich, B. Dewals   +9 more
wiley   +1 more source

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

Journal of Forecasting, Volume 45, Issue 7, Page 3170-3190, November 2026.
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

Interpreting Non‐Linear Compaction Mechanics of Expansive Soils Using Reliability Calibrated Framework

Applied Research, Volume 5, Issue 5, October 2026.
This study introduces a reliability‐calibrated machine learning framework to predict the maximum dry density and optimum moisture content of heterogeneous soils. By integrating 15 algorithms with conformal prediction, the models deliver accurate compaction estimates alongside rigorous, distribution‐free 90% confidence intervals.
Farjad Aziz, Muhammad Waseem, Zeeshan Asghar, Megersa Kebede Leta   +3 more
wiley   +1 more source

Joint count transformation models with covariate‐dependent correlations

Ecology, Volume 107, Issue 10, October 2026.
Abstract Joint species distribution models are essential for understanding how ecological covariates shape species communities. However, most existing approaches are limited by rigid parametric distributions for count data and the inability to model how interspecific associations change with those covariates.
Lukas Graz, Luisa Barbanti, Roland Brandl, Torsten Hothorn   +3 more
wiley   +1 more source

Prior elicitation in Bayesian quantile regression for longitudinal data [PDF]

, 2011
© 2011 Alhamzawi R, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original auhor and source are ...
Alhamzawi, Rahim, Rahim Alhamzawi, Pan, J, Yu, K, Yu, Keming, Pan, Jianxin, Al-Hamzawi, R, Keming Yu   +7 more
core   +1 more source

Harnessing Large‐Scale Multi‐Omics Data for Risk Prediction and Deep Phenotyping of Valvular Heart Diseases in the General Population

Advanced Science, Volume 13, Issue 53, 24 September 2026.
Large‐scale UK Biobank analyses identify clinical and proteomic signatures for early prediction of valvular heart disease and its subtypes. Proteins add predictive value for VHD, AVS, and MVR, with outcome‐specific compact panels showing translational potential. Multi‐layer evidence highlights matrix remodeling, protease regulation, immune inflammation,
Zhihao Jiang, Yang Liu, Mingyu Song, Ning Chen, Canqing Yu, Jun Lv, Eric Yuk Fai Wan, Lu Qi, Liming Li, Dianjianyi Sun, Bang Zheng   +10 more
wiley   +1 more source