Results 111 to 120 of about 52,512 (299)
Employing Ridge Regression Procedure to Remedy the Multicollinearity Problem
Ibn Al-Haitham Journal for Pure and Applied Sciences, 2017 In this paper we introduce many different Methods of ridge regression to solve multicollinearity problem in linear regression model. These Methods include two types of ordinary ridge regression (ORR1), (ORR2) according to the choice of ridge ...
Hazim M. Gorgees, Bushra A. Ali
doaj
Energy GeoscienceIn oil and gas exploration, elucidating the complex interdependencies among geological variables is paramount. Our study introduces the application of sophisticated regression analysis method at the forefront, aiming not just at predicting geophysical ...
Yang Li +5 more
doaj +1 more source
"Improved Empirical Bayes Ridge Regression Estimators under Multicollinearity" [PDF]
In this paper we consider the problem of estimating the regression parameters in a multiple linear regression model when the multicollinearity is present.Under the assumption of normality, we present three empirical Bayes estimators.
Tatsuya Kubokawa, M. S. Srivastava
core
Generalized smooth monotonic regression [PDF]
, 2005 Common approaches to monotonic regression focus on the case of a unidimensional covariate and continuous dependent variable. Here a general approach is proposed that allows for additive and multiplicative structures where one or more variables have ...
Gerhard Tutz +3 more
core +1 more source
Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories
Advanced Energy Materials, EarlyView.In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen +4 more
wiley +1 more source
"Minimax Empirical Bayes Ridge-Principal Component Regression Estimators" [PDF]
In this paper, we consider the problem of estimating the regression parameters in a multiple linear regression model with design matrix A when the multicollinearity is present.
Tatsuya Kubokawa, M. S. Srivastava
core
, 2004 The main problem with localized discriminant techniques is the curse of dimensionality, which seems to restrict their use to the case of few variables. This restriction does not hold if localization is combined with a reduction of dimension.
Binder, Harald, Tutz, Gerhard
core +1 more source
Advanced Intelligent Discovery, EarlyView.This work establishes a correlation between solvent properties and the charge transport performance of solution‐processed organic thin films through interpretable machine learning. Strong dispersion interactions (δD), moderate hydrogen bonding (δH), closely matching and compatible with the solute (quadruple thiophene), and a small molar volume (MolVol)
Tianhao Tan, Lian Duan, Dong Wang
wiley +1 more source
Model selection in kernel ridge regression [PDF]
Computational Statistics & Data Analysis, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +4 more sources
The Challenge of Handling Structured Missingness in Integrated Data Sources
Advanced Intelligent Discovery, EarlyView.As data integration becomes ever more prevalent, a new research question that emerges is how to handle missing values that will inevitably arise in these large‐scale integrated databases? This missingness can be described as structured missingness, encompassing scenarios involving multivariate missingness mechanisms and deterministic, nonrandom ...
James Jackson +6 more
wiley +1 more source