Results 11 to 20 of about 25,104 (288)
Ridge regression and its applications in genetic studies.
PLoS ONE, 2021 With the advancement of technology, analysis of large-scale data of gene expression is feasible and has become very popular in the era of machine learning. This paper develops an improved ridge approach for the genome regression modeling.
M Arashi +3 more
doaj +1 more source
Superiority of the MCRR Estimator Over Some Estimators In A Linear Model [PDF]
المجلة العراقية للعلوم الاحصائية, 2011 Modified (r, k) class ridge regression (MCRR) which includes unbiased ridge regression (URR), (r, k) class, principal components regression (PCR) and the ordinary least squares (OLS) estimators is proposed in regression analysis, to overcome the problem ...
Feras Sh. M. Batah
doaj +1 more source
Correlation Based Ridge Parameters in Ridge Regression with Heteroscedastic Errors and Outliers [PDF]
Journal of Statistical Theory and Applications (JSTA), 2015 This paper introduces some new estimators for estimating ridge parameter, based on correlation between response and regressor variables for ridge regression analysis.
A.V. Dorugade
doaj +1 more source
Minimax Ridge Regression Estimation. [PDF]
The Annals of Statistics, 1977 The technique of ridge regression, first proposed by Hoerl and Kennard, has become a popular tool for data analysts faced with a high degree of multicollinearity in their data. By using a ridge estimator, one hopes to both stabilize one's estimates (lower the condition number of the design matrix) and improve upon the squared error loss of the least ...
openaire +2 more sources
Robust weighted ridge regression based on S – estimator
African Scientific Reports, 2023 Ordinary least squares (OLS) estimator performance is seriously threatened by correlated regressors often called multicollinearity. Multicollinearity is a situation when there is strong relationship between any two exogenous variables.
Taiwo Stephen Fayose +3 more
doaj +1 more source
Applications of Some Improved Estimators in Linear Regression [PDF]
, 2005 The problem of estimation of the regression coefficients under multicollinearity situation for the restricted linear model is discussed. Some improve estimators are considered, including the unrestricted ridge regression estimator (URRE), restricted ...
Kibria, B. M. Golam
core +2 more sources
F1000Research, 2021 Background: Multicollinearity greatly affects the Maximum Likelihood Estimator (MLE) efficiency in both the linear regression model and the generalized linear model. Alternative estimators to the MLE include the ridge estimator, the Liu estimator and the
Olukayode Adebimpe +4 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
Ridge-Type Estimators for Regression Analysis
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1974 Summary An examination of the mean-square error properties of a class of shrinkage estimators for the normal regression model leads to a new derivation of the Hoerl–Kennard (1970) Ridge estimator and its generalization. Comparison is made with the James–Stein estimator, and with the generalized-inverse estimator proposed by Marquardt ...
Goldstein, M., Smith, A. F. M.
openaire +2 more sources
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