Results 41 to 50 of about 2,638,357 (294)

A New Two-Parameter Estimator for Beta Regression Model: Method, Simulation, and Application

Frontiers in Applied Mathematics and Statistics, 2022
The beta regression is a widely known statistical model when the response (or the dependent) variable has the form of fractions or percentages. In most of the situations in beta regression, the explanatory variables are related to each other which is ...
Mohamed R. Abonazel, Zakariya Yahya Algamal, Fuad A. Awwad, Ibrahim M. Taha   +3 more
doaj   +1 more source

Inhibition of cyclin‐dependent kinases 12/13 using CT7439 as a treatment for colorectal cancer with CDK12 upregulation

Molecular Oncology, EarlyView.
The proposed mechanism of action for the CDK12/13 inhibitor and cyclin K degrader, CT7439. CDK12/13 inhibition interrupts transcription elongation, leading to increased DNA damage that results in cell death. This agent is a potentially novel treatment option for patients with colorectal cancer. Created in BioRender. Cyclin‐dependent kinase (CDK) 12 and
Wylie K. Watlington, Divya L. Dayanidhi, Mohammad Zokaasadi, John B. Mantyh, Pelumi D. Olawuni, Gabrielle Rupprecht, Jeremy M. Force, Shannon McCall, Ashwani K. Bahl, David S. Hsu, Jason A. Somarelli   +10 more
wiley   +1 more source

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 New Tobit Ridge-Type Estimator of the Censored Regression Model With Multicollinearity Problem

Frontiers in Applied Mathematics and Statistics, 2022
In the censored regression model, the Tobit maximum likelihood estimator is unstable and inefficient in the occurrence of the multicollinearity problem.
Issam Dawoud, Mohamed R. Abonazel, Fuad A. Awwad, Elsayed Tag Eldin   +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

A Suggested Biased Estimator for Correcting Multicollinearity in Multinomial Logistic Regression [PDF]

The Egyptian Statistical Journal, 2014
Multinomial logistic model suffers from multicollinearity that causes wider confidence intervals and incorrect decisions for testing hypotheses for the regression parameters.
Rasha A.Farghali
doaj   +1 more source

M Robust Weighted Ridge Estimator in Linear Regression Model

African Scientific Reports, 2023
Correlated regressors are a major threat to the performance of the conventional ordinary least squares (OLS) estimator. The ridge estimator provides more stable estimates in this circumstance.
Taiwo Stephen Fayose, Kayode Ayinde, Olatayo Olusegun Alabi   +2 more
doaj   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +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