Results 81 to 90 of about 113,685 (321)

Uncertainty Calibration in Molecular Machine Learning: Comparing Evidential and Ensemble Approaches

Chemistry – A European Journal, EarlyView.
Raw uncertainty estimates from deep evidential regression and deep ensembles are systematically miscalibrated. Post hoc calibration aligns predicted uncertainty with true errors, improving reliability and enabling efficient active learning and reducing computational cost while preserving predictive accuracy.
Bidhan Chandra Garain, Max Pinheiro Jr., Matheus de Oliveira Bispo, Mario Barbatti   +3 more
wiley   +1 more source

Bayesian Estimation of Archimedean Copula-Based SUR Quantile Models

Complexity, 2020
We propose a high-dimensional copula to model the dependence structure of the seemingly unrelated quantile regression. As the conventional model faces with the strong assumption of the multivariate normal distribution and the linear dependence structure,
Nachatchapong Kaewsompong, Paravee Maneejuk, Woraphon Yamaka   +2 more
doaj   +1 more source

Childhood exposures to environmental chemicals and neurodevelopmental outcomes in congenital heart disease.

PLoS ONE, 2022
BackgroundChildren with congenital heart defects have an increased risk of neurodevelopmental disability. The impact of environmental chemical exposures during daily life on neurodevelopmental outcomes in toddlers with congenital heart defects is unknown.
J William Gaynor, Nancy B Burnham, Richard F Ittenbach, Marsha Gerdes, Judy C Bernbaum, Elaine Zackai, Daniel J Licht, William W Russell, Erin E Zullo, Thomas Miller, Hakon Hakonarson, Kayan A Clarke, Gail P Jarvik, Antonia M Calafat, Asa Bradman, David C Bellinger, Frederick M Henretig, Eric S Coker   +17 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

COMPARISON BETWEEN BAYESIAN QUANTILE REGRESSION AND BAYESIAN LASSO QUANTILE REGRESSION FOR MODELING POVERTY LINE WITH PRESENCE OF HETEROSCEDASTICITY IN WEST SUMATRA

Barekeng
The poverty line is the threshold income level below which a person or household is considered to be living in poverty. The poverty line is a representation of the minimum rupiah amount needed to meet the minimum basic food needs equivalent to 2100 ...
Lilis Harianti Hasibuan, Ferra Yanuar, Dodi Devianto, Maiyastri Maiyastri, Rudiyanto Rudiyanto   +4 more
doaj   +1 more source

Changes in extreme rainfall and its implications for design rainfall using a Bayesian quantile regression approach

, 2020
This study aims to explore possible distributional changes in annual daily maximum rainfalls (ADMRs) over South Korea using a Bayesian multiple non-crossing quantile regression model.
Sumiya Uranchimeg, H. Kwon, Byungsik Kim, Tae‐Woong Kim   +3 more
semanticscholar   +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

Discerning excellence from mediocrity in swimming: New insights using Bayesian quantile regression

European Journal of Sport Science, 2020
Purpose: Previous research has captured point estimates for population means of somatic variables associated with swimming speed across strokes, but have not determined if predictors of swimming speed operate the same at the upper tails of the ...
Tony D. Myers, Y. Negra, Senda Sammoud, H. Chaabène, A. Nevill   +4 more
semanticscholar   +1 more source

Joint modelling of annual precipitation maxima over several durations for the construction of intensity–duration–frequency curves

Canadian Journal of Statistics, EarlyView.
Abstract Intensity–duration–frequency curves are used by a wide range of professionals to manage the risks related to extreme rainfall. In Canada, these curves are produced by Environment and Climate Change Canada on the basis of Gumbel distributions fitted independently for each accumulation period.
Paul Mathivon, Christian Genest, Jonathan Jalbert   +2 more
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