Results 1 to 10 of about 4,611 (130)

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

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

Scientific Reports
Dr. Nadeem Akhtar, Muteb Faraj Alharthi
exaly   +2 more sources

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

A lava attack on the recovery of sums of dense and sparse signals [PDF]

, 2015
Common high-dimensional methods for prediction rely on having either a sparse signal model, a model in which most parameters are zero and there are a small number of non-zero parameters that are large in magnitude, or a dense signal model, a model with ...
Chernozhukov, Victor, Hansen, Christian, Liao, Yuan   +2 more
core   +3 more sources

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  

Boosting Ridge Regression [PDF]

, 2005
Ridge regression is a well established method to shrink regression parameters towards zero, thereby securing existence of estimates. The present paper investigates several approaches to combining ridge regression with boosting techniques.
Binder, Harald, Tutz, Gerhard
core   +2 more sources