Results 11 to 20 of about 3,505 (256)
Scientific African, 2022 The parameters in the Poisson regression model are usually estimated using the maximum likelihood estimator (MLE). MLE suffers a breakdown when there is either multicollinearity or outliers in the Poisson regression model.
Kingsley C Arum +2 more
doaj +1 more source
Modified Jackknifed Ridge Estimator in Bell Regression Model: Theory, Simulation and Applications
Iraqi Journal for Computer Science and Mathematics, 2023 Regression models explore the relationship between the response variable and one or more explanatory variables. It becomes practically challenging in real-life applications to model this relationship when the explanatory variables are linearly dependent.
Zakariya Algamal +3 more
doaj +1 more source
Predictive efficiency of ridge regression estimator [PDF]
Yugoslav Journal of Operations Research, 2017 In this article we have considered the problem of prediction within and outside the sample for actual and average values of the study variables in case of ordinary least squares and ridge regression estimators.
Tiwari Manoj, Sharma Amit
doaj +1 more source
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
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
Treating Multicollinearity Problem Using Gool Programming Technique [PDF]
The Egyptian Statistical Journal, 2011 Multiple regression analysis is usually efficient for prediction, but often produces poor results because of the multicollinearity among the independent variables.
Afaf El-Dash +2 more
doaj +1 more source