Results 41 to 50 of about 134 (94)

Special ridge-type estimator: Simulation and application to chemical data

AIP Advances
This study delves into regularization techniques, such as ridge regression, Liu estimator, and Kibria–Lukman estimator, as alternatives to the maximum likelihood method for addressing multicollinearity in beta regression models.
Rasha A. Farghali, Adewale F. Lukman, Haadi Abdul-Rahaman, Zakariya Yahya Algamal, Samah M. Abo-El-Hadid   +4 more
doaj   +1 more source

A Comparative Study of Some Two –Parameter Ridge-Type and Liu-Type Estimators to Combat Multicollinearity Problem in Regression Models: Simulation and Application [PDF]

المجلة العلمية للاقتصاد والتجارة
In multiple linear regression analysis, the ordinary least squares (OLS) method has been the most popular technique for estimating parameters of linear regression model due to its optimal properties.
Wael Saad Hsanein El-doakly
doaj   +1 more source

Mitigating multicollinearity in ridge regression: a comparative study of quantile-based two parameter weighted KL-estimators

Research in Statistics
In linear regression, when predictors exhibit collinearity, the problem of multicollinearity arises, leading to a reduction in the efficiency of the ordinary least squares (OLS) estimator.
Qamruz Zaman, Danish Wasim, B. M. Golam Kibria, Maha Shabbir, Naveed Ullah   +4 more
doaj   +1 more source

A New Biased Estimation Class to Combat the Multicollinearity in Regression Models: Modified Two--Parameter Liu Estimator

Computational Journal of Mathematical and Statistical Sciences
The multicollinearity problem occurrence of the explanatory variables affects the least-squares (LS) estimator seriously in the regression models. The multicollinearity adverse effects on the LS estimation are also investigated by many authors.
Mohamed Reda Abonazel
doaj   +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

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

Robust-stein estimator for overcoming outliers and multicollinearity. [PDF]

Sci Rep, 2023
Lukman AF, Farghali RA, Kibria BMG, Oluyemi OA.   +3 more
europepmc   +1 more source

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

Behaviormetrika, 2023
Ueno M.
europepmc   +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

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