Results 61 to 70 of about 10,357 (300)

Use of regularized quantile regression to predict the genetic merit of pigs for asymmetric carcass traits [PDF]

Pesquisa Agropecuária Brasileira, 2018
: The objective of this work was to evaluate the use of regularized quantile regression (RQR) to predict the genetic merit of pigs for asymmetric carcass traits, compared with the Bayesian lasso (Blasso) method.
Patricia Mendes dos Santos, Ana Carolina Campana Nascimento, Moysés Nascimento, Fabyano Fonseca e Silva, Camila Ferreira Azevedo, Rodrigo Reis Mota, Simone Eliza Facioni Guimarães, Paulo Sávio Lopes   +7 more
doaj   +1 more source

Rank‐based estimation of propensity score weights via subclassification

Canadian Journal of Statistics, EarlyView.
Abstract Propensity score (PS) weighting estimators are widely used for causal effect estimation and enjoy desirable theoretical properties, such as consistency and potential efficiency under correct model specification. However, their performance can degrade in practice due to sensitivity to PS model misspecification.
Linbo Wang, Yuexia Zhang, Thomas S. Richardson, Xiaohua Zhou   +3 more
wiley   +1 more source

BeQut: Bayesian Estimation for Quantile Regression Mixed Models [PDF]

, 2023
Antoine Barbieri, Hélène Jacqmin‐Gadda   +1 more
openalex   +1 more source

On subset least squares estimation and prediction in vector autoregressive models with exogenous variables

Canadian Journal of Statistics, EarlyView.
Abstract We establish the consistency and the asymptotic distribution of the least squares estimators of the coefficients of a subset vector autoregressive process with exogenous variables (VARX). Using a martingale central limit theorem, we derive the asymptotic normal distribution of the estimators. Diagnostic checking is discussed using kernel‐based
Pierre Duchesne, Thanh‐Liem Huynh, Mylène Oscar   +2 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

Rockburst prediction based on data preprocessing and hyperband‐RNN‐DNN

Deep Underground Science and Engineering, EarlyView.
A data preprocessing workflow is proposed to address challenges in rockburst data analysis. Coupled algorithms preprocess the data set, and hyperband optimization is used to enhance RNN performance. Results show that preprocessing improves accuracy, while dense layers enhance model stability and prediction performance.
Yong Fan, Chenxi Yin, Guangdong Yang, Shengyong Ding, Bin Tian   +4 more
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

The Impact of Uncertainty on Forecasting the US Economy

Journal of Forecasting, EarlyView.
ABSTRACT This paper examines the predictive value of uncertainty measures for key macroeconomic indicators across multiple forecast horizons. We evaluate how different uncertainty proxies—economic policy uncertainty (EPU), VIX, geopolitical risk, and measures of macroeconomic and financial uncertainty—enhance forecast accuracy for industrial production,
Angelica Ghiselli
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