Results 31 to 40 of about 33,887 (293)
Two-Parameter Modified Ridge-Type M-Estimator for Linear Regression Model
The Scientific World Journal, 2020 The general linear regression model has been one of the most frequently used models over the years, with the ordinary least squares estimator (OLS) used to estimate its parameter.
Adewale F. Lukman +3 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 +2 more
core +3 more sources
A new modified ridge-type estimator for the beta regression model: simulation and application
AIMS Mathematics, 2022 The beta regression model has become a popular tool for assessing the relationships among chemical characteristics. In the BRM, when the explanatory variables are highly correlated, then the maximum likelihood estimator (MLE) does not provide reliable ...
Muhammad Nauman Akram +3 more
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 +2 more
doaj +1 more source
The effect of high leverage points on the logistic ridge regression estimator having multicollinearity [PDF]
, 2013 This article is concerned with the performance of logistic ridge regression estimation technique in the presence of multicollinearity and high leverage points.
Ariffin @ Mat Zin, Syaiba Balqish +1 more
core +1 more source
A New Type Iterative Ridge Estimator: Applications and Performance Evaluations
Journal of Mathematics, 2022 The usage of the ridge estimators is very common in presence of multicollinearity in multiple linear regression models. The ridge estimators are used as an alternative to ordinary least squares in case of multicollinearity as they have lower mean square ...
Aydın Karakoca
doaj +1 more source
Ibn Al-Haitham Journal for Pure and Applied Sciences, 2018 In the presence of multi-collinearity problem, the parameter estimation method based on the ordinary least squares procedure is unsatisfactory. In 1970, Hoerl and Kennard insert analternative method labeled as estimator of ridge regression.
Hazim Mansoor Gorgees +1 more
doaj +1 more source
Research in StatisticsThis 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
Kuwait Journal of Science, 2023 Ridge regression is employed to estimate the regression parameters while circumventing the multicollinearity among independent variables. The ridge parameter plays a vital role as it controls bias-variance tradeoff. Several methods for choosing the ridge
Irum Sajjad Dar +3 more
doaj +1 more source
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