Results 21 to 30 of about 221 (137)

Handling Multicollinearity and Outliers in Logistic Regression Using the Robust Kibria–Lukman Estimator

Axioms
Logistic regression models encounter challenges with correlated predictors and influential outliers. This study integrates robust estimators, including the Bianco–Yohai estimator (BY) and conditionally unbiased bounded influence estimator (CE), with the ...
Adewale F. Lukman, Suleiman Mohammed, Olalekan Olaluwoye, Rasha A. Farghali   +3 more
doaj   +2 more sources

A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications. [PDF]

Scientifica (Cairo), 2020
The ridge regression‐type (Hoerl and Kennard, 1970) and Liu‐type (Liu, 1993) estimators are consistently attractive shrinkage methods to reduce the effects of multicollinearity for both linear and nonlinear regression models. This paper proposes a new estimator to solve the multicollinearity problem for the linear regression model.
Kibria BMG, Lukman AF.
europepmc   +2 more sources

Two-Parameter Modified Ridge-Type M-Estimator for Linear Regression Model. [PDF]

ScientificWorldJournal, 2020
The general linear regression model has been one of the most frequently used models over the years, with the ordinary least squares estimator (OLS) used to estimate its parameter. The problems of the OLS estimator for linear regression analysis include that of multicollinearity and outliers, which lead to unfavourable results. This study proposed a two‐
Lukman AF, Ayinde K, Golam Kibria BM, Jegede SL.   +3 more
europepmc   +2 more sources

Quantile-based robust Kibria–Lukman estimator for linear regression model to combat multicollinearity and outliers: Real life applications using T20 cricket sports and anthropometric data

Kuwait Journal of Science
The performance of ordinary least squares (OLS) and ridge regression (RR) are influenced when outliers are present in y-direction with multicollinearity among independent variables. The robust RR with ridge parameters provides a biased estimator that has
Danish Wasim, Muhammad Suhail, Sajjad Ahmad Khan, Maha Shabbir, Fuad A. Awwad, Emad A.A. Ismail, Hijaz Ahmad, Amjad Ali   +7 more
doaj   +2 more sources

New insights into multicollinearity in the Cox proportional hazard models: the Kibria-Lukman estimator and its application. [PDF]

J Appl Stat
Seifollahi S, Algamal ZY, Arashi M.
europepmc   +2 more sources

Predictive Performance Evaluation of the Kibria-Lukman Estimator

WSEAS TRANSACTIONS ON MATHEMATICS, 2022
Regression models are commonly used in prediction, but their predictive performances may be affected by the problem called the multicollinearity. To reduce the effect of the multicollinearity, different biased estimators have been proposed as alternatives to the ordinary least squares estimator.
Issam Dawoud, Mohamed R. Abonazel, Elsayed Tag Eldin   +2 more
openaire   +1 more source

New Robust Estimators for Handling Multicollinearity and Outliers in the Poisson Model: Methods, Simulation and Applications

Axioms, 2022
The Poisson maximum likelihood (PML) is used to estimate the coefficients of the Poisson regression model (PRM). Since the resulting estimators are sensitive to outliers, different studies have provided robust Poisson regression estimators to alleviate ...
Issam Dawoud, Fuad A. Awwad, Elsayed Tag Eldin, Mohamed R. Abonazel   +3 more
doaj   +1 more source

A new hybrid estimator for linear regression model analysis: Computations and simulations

Scientific African, 2023
The Linear regression model explores the relationship between a response variable and one or more independent variables. The parameters in the model are often estimated using the Ordinary Least Square Estimator (OLSE).
G.A. Shewa, F.I. Ugwuowo
doaj   +1 more source

Robust biased estimators for Poisson regression model: Simulation and applications

Concurrency and Computation: Practice and Experience, Volume 35, Issue 7, 25 March 2023., 2023
Summary The method of maximum likelihood flops when there is linear dependency (multicollinearity) and outlier in the generalized linear models. In this study, we combined the ridge estimator with the transformed M‐estimator (MT) and the conditionally unbiased bounded influence estimator (CE).
Adewale F. Lukman, Mohammad Arashi, Vilmos Prokaj   +2 more
wiley   +1 more source

Jackknifing K-L estimator in Poisson regression model [PDF]

, 2022
At the point when there is collinearity between the reaction variable and various illustrative factors, displaying the connection between the reaction variable and a few informative factors is troublesome.
Algamal, Zakariya Yahya, Hamad, Abed Ali
core   +2 more sources