Results 41 to 50 of about 300,687 (184)

Modified Liu Parameters for Scaling Options of the Multiple Regression Model with Multicollinearity Problem

Mathematics
The multiple regression model statistical technique is employed to analyze the relationship between the dependent variable and several independent variables.
Autcha Araveeporn
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, Midi, Habshah   +1 more
core   +1 more source

Diet, blood pressure, and multicollinearity. [PDF]

Hypertension, 1984
Recent reports of an inverse association between dietary calcium intake and hypertension stimulated this analysis of the relationship of blood pressure to more than 20 dietary factors among a group of 8000 Japanese men in Hawaii. Reported intakes of potassium, calcium, protein, and milk were all inversely associated with blood pressure levels when ...
D, Reed, D, McGee, K, Yano, J, Hankin
openaire   +2 more sources

Estimation under Multicollinearity: Application of Restricted Liu and Maximum Entropy Estimators to the Portland Cement Dataset [PDF]

A high degree of multicollinearity among the explanatory variables severely impairs estimation of regression coefficients by the Ordinary Least Squares. Several methods have been suggested to ameliorate the deleterious effects of multicollinearity.
Mishra, SK
core   +1 more source

Novel Statistical Regularized Extreme Learning Algorithm to Address the Multicollinearity in Machine Learning

IEEE Access
The multicollinearity problem is a common phenomenon in data-driven studies, significantly affecting the performance of machine learning algorithms during the process of extracting information from data.
Hasan Yildirim
doaj   +1 more source

Multicollinearity and maximum entropy leuven estimator [PDF]

Multicollinearity is a serious problem in applied regression analysis. Q. Paris (2001) introduced the MEL estimator to resolve the multicollinearity problem.
Sougata Poddar
core  

A New Ridge Regression Causality Test in the Presence of Multicollinearity [PDF]

This paper analyzes and compares the properties of the most commonly applied versions of the Granger causality (GC) test to a new ridge regression GC test (RRGC), in the presence of multicollinearity.
Månsson, Kristofer, Shukur, Ghazi, Sjölander, Pär   +2 more
core   +1 more source

Multicollinearity

, 2023
In this paper, I explore the symptoms of multicollinearity, detection methods for multiple linear regression models, examples using simulated and real-world data, and possible remedies.
openaire   +1 more source

"Improved Empirical Bayes Ridge Regression Estimators under Multicollinearity" [PDF]

In this paper we consider the problem of estimating the regression parameters in a multiple linear regression model when the multicollinearity is present.Under the assumption of normality, we present three empirical Bayes estimators.
M. S. Srivastava, Tatsuya Kubokawa
core  

ESTIMASI PARAMETER REGRESI RIDGE MENGGUNAKAN ITERASI HOERL, KENNARD, DAN BALDWIN (HKB) UNTUK PENANGANAN MULTIKOLINIERITAS (Studi Kasus Pengaruh BI Rate, Jumlah Uang Beredar, dan Nilai Tukar Rupiah terhadap Tingkat Inflasi di Indonesia) [PDF]

, 2015
Regression analysis is statistical method used to analyze the dependence of respond variables to predictor variable. In multiple linear regression analysis, there are assumptions that must be met, they are normality, homoscedasticity, absence of ...
Solekakh, Nur Aeniatus
core