Results 31 to 40 of about 551 (134)

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.
Ayanlowo, E.A, Oladapo, D.I, P. Sa, Obadina, G.O   +3 more
semanticscholar   +2 more sources

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
T. J. Adejumo, K. Ayinde, A. I. Okegbade, A. Akomolafe, Opeoluwa Abosede Oshuporu, S. O. Koleoso   +5 more
semanticscholar   +3 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

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

Behaviormetrika, 2023
A. Lukman, M. Norouzirad, F. Marques, D. Mazarei   +3 more
semanticscholar   +2 more sources

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

A New Type Iterative Ridge Estimator: Applications and Performance Evaluations

Journal of Mathematics, Volume 2022, Issue 1, 2022., 2022
The usage of the ridge estimators is very common in presence of multicollinearity in multiple linear regression models. The ridge estimators are used as an alternative to ordinary least squares in case of multicollinearity as they have lower mean square error.
Aydın Karakoca, Niansheng Tang
wiley   +1 more source

Performance of the Ridge and Liu Estimators in the zero‐inflated Bell Regression Model

Journal of Mathematics, Volume 2022, Issue 1, 2022., 2022
The Poisson regression model is popularly used to model count data. However, the model suffers drawbacks when there is overdispersion—when the mean of the Poisson distribution is not the same as the variance. In this situation, the Bell regression model fits well to the data. Also, there is a high tendency of excess zeros in the count data.
Zakariya Yahya Algamal, Adewale F. Lukman, Mohamed R. Abonazel, Fuad A. Awwad, Niansheng Tang   +4 more
wiley   +1 more source