Results 41 to 50 of about 25,104 (288)

Performance of some estimators for the multicollinear logistic regression model: theory, simulation, and applications

Research in Statistics
This article proposes some new estimators, namely Stein’s estimators for ridge regression and Kibria and Lukman estimator and compares their performance with some existing estimators, namely maximum likelihood estimator (MLE), ridge regression estimator,
Md Ariful Hoque, B. M. Golam Kibria
doaj   +1 more source

A stochastic restricted ridge regression estimator

Journal of Multivariate Analysis, 2009
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +1 more source

Large‐scale bidirectional arrayed genetic screens identify OXR1 and EMC4 as modifiers of αSynuclein aggregation

FEBS Open Bio, EarlyView.
Activation of the mitochondrial protein OXR1 increases pSyn129 αSynuclein aggregation by lowering ATP levels and altering mitochondrial membrane potential, particularly in response to MSA‐derived fibrils. In contrast, ablation of the ER protein EMC4 enhances autophagic flux and lysosomal clearance, broadly reducing α‐synuclein aggregates.
Sandesh Neupane, Lea Nikolić, Lorenzo Maraio, Thomas Goiran, Nathan Karpilovsky, Stefano Sellitto, Vangelis Bouris, Jiang‐An Yin, Ronald Melki, Edward A Fon, Adriano Aguzzi, Elena De Cecco   +11 more
wiley   +1 more source

Modified One-Parameter Liu Estimator for the Linear Regression Model

Modelling and Simulation in Engineering, 2020
Motivated by the ridge regression (Hoerl and Kennard, 1970) and Liu (1993) estimators, this paper proposes a modified Liu estimator to solve the multicollinearity problem for the linear regression model.
Adewale F. Lukman, B. M. Golam Kibria, Kayode Ayinde, Segun L. Jegede   +3 more
doaj   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

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, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

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, Croux, Christophe, Öllerer, Viktoria   +2 more
core   +2 more sources

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, Zhenfeng Li, Yue Fei, Ali Mohammadi, Ge Li   +4 more
wiley   +1 more source

Biased proportional hazard regression estimator in the existence of collinearity

Heliyon, 2023
This paper proposed a new biased proportional hazard regression (PHR) estimator which is the combination of elastic net proportional hazard regression (ENPHR) and principal components proportional hazard regression (PCPHR) estimator.
Anu Sirohi, Basim S.O. Alsaedi, Marwan H. Ahelali, Mahesh Kumar Jayaswal   +3 more
doaj   +1 more source

On the Estimation of Derivatives Using Plug-in Kernel Ridge Regression Estimators

J. Mach. Learn. Res., 2020
We study the problem of estimating the derivatives of a regression function, which has a wide range of applications as a key nonparametric functional of unknown functions. Standard analysis may be tailored to specific derivative orders, and parameter tuning remains a daunting challenge particularly for high-order derivatives.
Zejian Liu, Meng Li
openaire   +4 more sources