Results 81 to 90 of about 1,043 (255)

A comparative study of the performance of new ridge estimators for multicollinearity: Insights from simulation and real data application

AIP Advances
This paper addresses the challenge of multicollinearity in regression models, a condition that inflates the standard errors of coefficients, leading to unreliable estimates and wider confidence intervals.
Nadeem Akhtar, Muteb Faraj Alharthi
doaj   +1 more source

(Non) Linear Regression Modeling [PDF]

We will study causal relationships of a known form between random variables. Given a model, we distinguish one or more dependent (endogenous) variables Y = (Y1, . . .
Čížek, Pavel
core  

Performance of ridge regression estimator methods on small sample size by varying correlation coefficients: a simulation study

, 2014
When independent variables have high linear correlation in a multiple linear regression model, we can have wrong analysis. It happens if we do the multiple linear regression analysis based on common Ordinary Least Squares (OLS) method. In this situation,
Fitrianto, Anwar, Lee, Ceng Yik
core   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

A modified ridge m-estimator for linear regression model with multicollinearity and outliers

, 2017
The ordinary least-square estimators for linear regression analysis with multicollinearity and outliers lead to unfavorable results. In this article, we propose a new robust modified ridge M-estimator (MRME) based on M-estimator (ME) to deal with the ...
Ertaş, Hasan, Hasan Ertaş
core   +1 more source

Efficient two-parameter estimator in linear regression model [PDF]

, 2019
In this article, two-parameter estimators in linear model with multicollinearity are considered. An alternative efficient two-parameter estimator is proposed and its properties are examined. Furthermore, this was compared with the ordinary least squares (
Dorugade, Ashok V.
core   +1 more source

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

Advanced Intelligent Discovery, EarlyView.
This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley   +1 more source

NEWLY PROPOSED ESTIMATOR FOR RIDGE PARAMETER: AN APPLICATION TO THE NIGERIAN ECONOMY [PDF]

, 2018
Different methodshave been adopted in the estimation of ridge parameter in ordinary ridge regression estimator. In this study new ridge parameter was introduced and evaluated via simulation study and application to real life data. The proposed
Olatunji, A., Lukman, A. F.
core  

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

New Two-Parameter Ridge Estimators for Addressing Multicollinearity in Linear Regression: Theory, Simulation, and Applications

Stats
Multicollinearity among explanatory variables often undermines the reliability of the ordinary least squares (OLS) estimator that can be used in linear regression modeling. To overcome the limitation, a variety of two-parameter estimation strategies have
Md Ariful Hoque, B. M. Golam Kibria, Zoran Bursac   +2 more
doaj   +1 more source