Results 61 to 70 of about 3,187,155 (299)

Performance of the Ridge and Liu Estimators in the zero-inflated Bell Regression Model

Journal of Mathematics, 2022
The Poisson regression model is popularly used to model count data. However, the model suffers drawbacks when there is overdispersion—when the mean of the Poisson distribution is not the same as the variance.
Zakariya Yahya Algamal, Adewale F. Lukman, Mohamed R. Abonazel, Fuad A. Awwad   +3 more
doaj   +1 more source

Machine Learning Models of COVID-19 Cases in the United States: A Study of Initial Lockdown and Reopen Regimes

Applied Sciences, 2021
The purpose of this paper is to model the cases of COVID-19 in the United States from 13 March 2020 to 31 May 2020. Our novel contribution is that we have obtained highly accurate models focused on two different regimes, lockdown and reopen, modeling ...
Arnold Kamis, Yudan Ding, Zhenzhen Qu, Chenchen Zhang   +3 more
doaj   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source

Predicting antifreeze proteins with weighted generalized dipeptide composition and multi-regression feature selection ensemble

BMC Bioinformatics, 2021
Background Antifreeze proteins (AFPs) are a group of proteins that inhibit body fluids from growing to ice crystals and thus improve biological antifreeze ability. It is vital to the survival of living organisms in extremely cold environments.
Shunfang Wang, Lin Deng, Xinnan Xia, Zicheng Cao, Yu Fei   +4 more
doaj   +1 more source

Ridge Estimation's Effectiveness for Multiple Linear Regression with Multicollinearity: An Investigation Using Monte-Carlo Simulations

Journal of Nigerian Society of Physical Sciences, 2021
The goal of this research is to compare multiple linear regression coefficient estimations with multicollinearity. In order to quantify the effectiveness of estimations by the mean of average mean square error, the ordinary least squares technique (OLS),
O. G. Obadina, Adedayo Funmi Adedotuun, O. A. Odusanya   +2 more
doaj   +1 more source

Data‐Driven Modeling of Composition–Processing–Microstructure Relations for Recycled Aluminum Cast Alloys

Advanced Science, EarlyView.
Interpretable machine learning reveals how composition and processing govern the formation and microstructural burden of Fe‐rich intermetallic compounds in recycled Al–Si–Fe–Mn alloys. By separating morphology selection from morphology‐conditioned burden partitioning, this framework shows that identical Fe contents can yield different intermetallic ...
Jaemin Wang, Waleed Mohammed, Dierk Raabe   +2 more
wiley   +1 more source

Optimization of ridge parameters in multivariate generalized ridge regression by plug-in methods

Hiroshima Mathematical Journal, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nagai, Isamu, Yanagihara, Hirokazu, Satoh, Kenichi   +2 more
openaire   +3 more sources

Physics‐Constrained Constitutive Learning of Rate‐Limiting Timescales for Efficient Hydrogen‐Based Direct Reduction for Green Steel Making

Advanced Science, EarlyView.
A conversion‐resolved constitutive framework is developed for the hydrogen‐based direct reduction of iron oxide pellets. Effective reaction and transport timescales are inferred directly from measured trajectories and mapped against operating conditions, pellet architecture, and composition. The analysis reveals how late‐stage transport control emerges
Anurag Bajpai, Barak Ratzker, Pasquale Cavaliere, Dierk Raabe   +3 more
wiley   +1 more source

Development of the generalized ridge estimator for the Poisson-Inverse Gaussian regression model with multicollinearity

Scientific Reports
The Poisson-Inverse Gaussian regression model is a widely used method for analyzing count data, particularly in over-dispersion. However, the reliability of parameter estimates obtained through maximum likelihood estimation in this model can be ...
Fatimah A. Almulhim, Ali T. Hammad, M. E. Bakr, Oluwafemi Samson Balogun, Alexis Habineza, M. M. Abd El-Raouf   +5 more
doaj   +1 more source

Comparative Study in Controlling Outliers and Multicollinearity Using Robust Performance Jackknife Ridge Regression Estimator Based on Generalized-M and Least Trimmed Square Estimator

Jambura Journal of Mathematics
Regression analysis is one of the statistical methods used to determine the causal relationship between one or more explanatory variables to the affected variable.
Gustina Saputri, Netti Herawati, Tiryono Ruby, Khoirin Nisa   +3 more
doaj   +1 more source