Results 11 to 20 of about 643 (115)

Enhancing accuracy in modelling highly multicollinear data using alternative shrinkage parameters for ridge regression methods. [PDF]

Sci Rep
Akhtar N, Alharthi MF.
europepmc   +2 more sources

A New Quantile-Based Approach for LASSO Estimation

Mathematics, 2023
Regularization regression techniques are widely used to overcome a model’s parameter estimation problem in the presence of multicollinearity. Several biased techniques are available in the literature, including ridge, Least Angle Shrinkage Selection ...
Ismail Shah, Hina Naz, Sajid Ali, Amani Almohaimeed, Showkat Ahmad Lone   +4 more
doaj   +1 more source

Penalty and Shrinkage Strategies Based on Local Polynomials for Right-Censored Partially Linear Regression

Entropy, 2022
This study aims to propose modified semiparametric estimators based on six different penalty and shrinkage strategies for the estimation of a right-censored semiparametric regression model.
Syed Ejaz Ahmed, Dursun Aydın, Ersin Yılmaz   +2 more
doaj   +1 more source

A Comparison of Model-Assisted Estimators, With and Without Data-Driven Transformations of Auxiliary Variables, With Application to Forest Inventory

Frontiers in Forests and Global Change, 2021
Forest information is requested at many levels and for many purposes. Sampling-based national forest inventories (NFIs) can provide reliable estimates on national and regional levels.
Magnus Ekström, Magnus Ekström, Mats Nilsson   +2 more
doaj   +1 more source

Survival prediction based on compound covariate under Cox proportional hazard models. [PDF]

PLoS ONE, 2012
Survival prediction from a large number of covariates is a current focus of statistical and medical research. In this paper, we study a methodology known as the compound covariate prediction performed under univariate Cox proportional hazard models.
Takeshi Emura, Yi-Hau Chen, Hsuan-Yu Chen   +2 more
doaj   +1 more source

Improved Penalty Strategies in Linear Regression Models

Revstat Statistical Journal, 2017
We suggest pretest and shrinkage ridge estimation strategies for linear regression models. We investigate the asymptotic properties of suggested estimators.
Bahadır Yüzbaşı , S. Ejaz Ahmed , Mehmet Güngör   +2 more
doaj   +1 more source

Performance of LASSO and Elastic net estimators in Misspecified Linear Regression Model

Ceylon Journal of Science, 2019
Ridge Estimator (RE) has been used as an alternative estimator for Ordinary Least Squared Estimator (OLSE) to handle multicollinearity problem in the linear regression model. However, it introduces heavy bias when the number of predictors is high, and it
M. Kayanan, P. Wijekoon
doaj   +1 more source

a simulation comparison of Ridge regression estimators with Lars

پژوهش‌های ریاضی, 2022
Introduction Regression analysis is a common method for modeling relationships between variables. Usually Ordinary Least Squares method is applied to estimate regression model parameters.
Roshanak Alimohammadi, Jaleh Bahari
doaj  

Penalty Strategies in Semiparametric Regression Models

Mathematical and Computational Applications
This study includes a comprehensive evaluation of six penalty estimation strategies for partially linear models (PLRMs), focusing on their performance in the presence of multicollinearity and their ability to handle both parametric and nonparametric ...
Ayuba Jack Alhassan, S. Ejaz Ahmed, Dursun Aydin, Ersin Yilmaz   +3 more
doaj   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source