Results 71 to 80 of about 218 (135)

On Some Ridge Regression Estimators for Logistic Regression Models [PDF]

, 2018
The purpose of this research is to investigate the performance of some ridge regression estimators for the logistic regression model in the presence of moderate to high correlation among the explanatory variables.
Williams, Ulyana P
core   +1 more source

A new shrinkage estimator in negative binomial regression model [PDF]

The ridge estimator has been consistently demonstrated to be an attractive shrinkage method to reduce the effects of multicollinearity. The negative binomial regression model (NBRM) is a well-known model in application when the response variable is a ...
Algamal, Zakariya Yahya, Ismael, Hasnaa Ahmed   +1 more
core   +3 more sources

Ridge Estimation’s E�ectiveness for Multiple Linear Regression with Multicollinearity: An Investigation Using Monte-Carlo Simulations [PDF]

, 2021
The goal of this research is to compare multiple linear regression coe cient estimation technique with multicollinearity. In order to quantify the e ectiveness of estimations by the mean of average mean square error, the ordinary least squares technique
Adedotun, Adedayo F., Obadina, O. G., Odusanya, Oluwaseun A.   +2 more
core  

Introduction to the Vol. 50, No. 2, 2023. [PDF]

Behaviormetrika, 2023
Ueno M.
europepmc   +1 more source

Regression Estimators under Joint Multicollinearity and Autocorrelation Conditions: The Two-Stage Kibria-Lukman Estimator as an Enhanced Approach

International Journal of Development Mathematics (IJDM)
Multicollinearity among predictors and autocorrelation in residuals present significant challenges to the reliability and accuracy of linear regression models. These issues cause traditional Ordinary Least Squares (OLS) estimators to yield inflated variances and biased parameter estimates, ultimately leading to unreliable statistical inferences.
Ayanlola E. Ayanlowo, Ifedayo D. Oladapo, Olugbenga Obadina, Peter Madu, N., Odeyemi, A. Sunday   +4 more
openaire   +1 more source

Evaluating the Efficiency of the Jackknife Kibria-Lukman M-Estimator: A Simulation-Based Comparative Analysis

Asian Research Journal of Mathematics
Although linear regression is frequently used in predictive analysis, the Ordinary Least Squares (OLS) estimator's accuracy is decreased by multicollinearity and outliers. In order to offer a reliable substitute, this study suggests the Jackknife Kibria-Lukman (JKL) M-Estimator, which combines Ridge shrinkage, Jackknife resampling, and M-estimation. In
Ayanlowo, E.A, Oladapo, D.I, Phillips, S.A., Obadina, G.O   +3 more
openaire   +1 more source

Unbiased K-L estimator for the linear regression model. [PDF]

F1000Res, 2021
Aladeitan B, Lukman AF, Davids E, Oranye EH, Kibria GBM.   +4 more
europepmc   +1 more source

SIMULATION OF COMPARATIVE STUDY OF JAMES-STEIN ESTIMATOR, RIDGE REGRESSION ESTIMATOR, AND MODIFIED KIBRIA LUKMAN ESTIMATOR IN HANDLING MULTICOLLINEARITY IN POISSON REGRESSION

International Journal of Applied Science and Engineering Review
Poisson regression is a statistical method used to analyze data with a response in the form of a count variable. The purpose of this study is to compare the performance of the Poisson James-Stein Estimator, Poisson Ridge Regression Estimator, and Poisson Modified Kibria-Lukman Estimator methods in dealing with multicollinearity using simulated data ...
M. Fikri Alyasa Zam Zami, Netti Herawati, null Misgiyati, null Nusyirwan   +3 more
openaire   +1 more source

Combining Kibria-Lukman and principal component estimators for the distributed lag models

Behaviormetrika, 2023
A. F. Lukman, M. Norouzirad, F. J. Marques, D. Mazarei   +3 more
openaire   +1 more source

Robust M Kibria Lukman estimator for linear regression model with outliers in the x-direction: simulations and applications

Science World Journal
The Ordinary Least Square (OLS) estimator remains Best Linear Unbiased Estimator (BLUE) when all the assumptions surrounding it stay intact, but at an iota of violation of the assumptions, it becomes inefficient and unstable. Some causes of the violation are the multicollinearity and the presence of extreme values (outliers).
Adejumo, Taiwo Joel, Ayinde , Kayode, Okegbade , Ayobami Ibukun, Akomolafe, A.A., Oshuporu , Opeoluwa Abosede, Koleoso , Sunday Olawale   +5 more
openaire   +2 more sources