Results 51 to 60 of about 25,397 (329)
Advanced Engineering Materials, EarlyView.Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
Scientific African, 2022 The multicollinearity problem occurrence of the explanatory variables affects the least-squares (LS) estimator seriously in the regression models. The multicollinearity adverse effects on the LS estimation are also investigated by lots of authors.
Issam Dawoud +2 more
doaj +1 more source
Multicollinearity and A Ridge Parameter Estimation Approach [PDF]
, 2016 One of the main goals of the multiple linear regression model, Y = Xβ + u, is to assess the importance of independent variables in determining their predictive ability.
Iguernane, Mohamed, Khalaf, Ghadban
core +2 more sources
Process‐Informed Analysis of As‐Built Metal Additive Surface Features
Advanced Engineering Materials, EarlyView.This article introduces a novel method for feature‐based surface texture characterisation directly incorporating manufacturing variables into the feature extraction workflow. This marks a major step towards identifying process‐specific surface properties and their influence on part function and hence a holistic understanding of process–structure ...
Theresa Buchenau +5 more
wiley +1 more source
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
Advanced Functional Materials, EarlyView.A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source
Recovering Jackknife Ridge Regression Estimates from OLS Results [PDF]
مجلة جامعة الانبار للعلوم الصرفة, 2014 The aim of this paper is addressing or recalculate the estimation methods in multiple linear regression model when there is a problem of Multicollinearity in this model like the ridge regression for Hoerl and Kannard, Baldwin estimator (HKB) and ...
Feras Sh. Mahmood
doaj +1 more source
Single‐ and Dual‐Atom Configurations in Atomically Dispersed Catalysts for Lithium–Sulfur Batteries
Advanced Functional Materials, EarlyView.Single‐atom and dual‐atom‐based atomically dispersed catalysts (ADCs) effectively address the shuttle effect and sluggish redox kinetics in Li–S batteries. With nearly 100% atomic utilization and tunable coordination environments, ADCs enhance LiPSs adsorption, lower conversion barriers, and accelerate sulfur redox reactions.
Haoyang Xu +4 more
wiley +1 more source
The efficiency of modified jackknife and ridge type regression estimators: a comparison [PDF]
Surveys in Mathematics and its Applications, 2008 A common problem in multiple regression models is multicollinearity, which produces undesirable effects on the least squares estimator. To circumvent this problem, two well known estimation procedures are often suggested in the literature.
Sharad Damodar Gore +2 more
doaj
The Influence Function of Penalized Regression Estimators [PDF]
, 2014 To perform regression analysis in high dimensions, lasso or ridge estimation are a common choice. However, it has been shown that these methods are not robust to outliers.
Alfons, Andreas +2 more
core +2 more sources
On the performance of two-parameter ridge estimators for handling multicollinearity problem in linear regression: Simulation and application [PDF]
, 2023 M. S. Khan +5 more
openalex +1 more source