Results 71 to 80 of about 134 (94)

Modified jackknife Kibria–Lukman estimator for the Poisson regression model

Concurrency and Computation: Practice and Experience, 2021
AbstractPoisson regression is one of the methods to analyze count data and, the regression parameters are usually estimated using the maximum likelihood (ML) method. However, the ML method is sensitive to multicollinearity. Multicollinearity occurs when there is linear dependency among the explanatory variables.
Henrietta Ebele Oranye, Fidelis Ifeanyi Ugwuowo   +1 more
openaire   +1 more source

A robust Kibria–Lukman estimator for linear regression model to combat multicollinearity and outliers

Concurrency and Computation: Practice and Experience, 2022
SummaryTo circumvent the problem of multicollinearity in regression models, a ridge‐type estimator is recently proposed in the literature, which is named as the Kibria–Lukman estimator (KLE). The KLE has better properties than the conventional ridge regression estimator. However, the presence of outliers in the data set may have some adverse effects on
Abdul Majid, Shakeel Ahmad, Muhammad Aslam 0010, Muhammad Kashif   +3 more
openaire   +1 more source

On the preliminary test Kibria-Lukman estimator for the linear regression model

Communications in Statistics Part B: Simulation and Computation
Jibo Wu, B M Golam Kibria
exaly   +2 more sources

Comparisons Among the Preliminary Test Kibria-Lukman Estimator Based on Three Large Sample Tests in the Linear Regression Model

Proceedings of the 2023 6th International Conference on Machine Learning and Natural Language Processing, 2023
Jibo Wu
exaly   +2 more sources

New Class of Kibria–Lukman Estimator for Addressing Multicollinearity in Poisson Regression Model

Chiang Mai Journal of Science
Count data are prevalent across various disciplines, and the Poisson regression model (PRM) is often employed to analyze such data due to its widespread popularity. The model’s parameters are typically estimated using the maximum likelihood estimator (MLE). However, when multicollinearity exists among the explanatory variables, MLE may lead to unstable
Ohud A. Alqasem, Ali T. Hammad, M.M. Abd El-Raouf, Ahmed M. Gemeay   +3 more
openaire   +1 more source

Enhancing process monitoring in the Conway–Maxwell–Poisson framework: A study of deviance residual-based control charts with the Kibria–Lukman estimator

Journal of Computational and Applied Mathematics
Ulduz Mammadova, Adewale Lukman
exaly   +2 more sources

On the jackknife Kibria-Lukman estimator for the linear regression model

Communications in Statistics - Simulation and Computation, 2021
Fidelis Ifeanyi Ugwuowo, Henrietta Ebele Oranye, Kingsley Chinedu Arum   +2 more
openaire   +1 more source

Generalized kibria-lukman estimator for multicollinearity in linear regression models: theoretical insights and comparative analysis

International Journal of Science and Technology Research Archive
Multicollinearity, a common issue in regression models caused by high correlations among explanatory variables, undermines the stability and reliability of traditional estimators like Ordinary Least Squares (OLS). This study investigates the Generalized Kibria-Lukman (GKL) estimator, introduced by Dawoud et al.
null Ayanlowo E.A, null Oladapo D.I, null Odeyemi A.S, null Obadina G.O   +3 more
exaly   +2 more sources

Improved survival analysis with shrinkage Kibria–Lukman estimators in the Cox model: Application to lung cancer data

Statistical Methods in Medical Research
The Cox proportional hazards regression model is a widely used and valuable tool for modeling survival time with predictors, however its performance can deteriorate in the presence of multicollinearity. It can lead to unreliable estimates from the maximum partial likelihood estimator.
Solmaz Seifollahi, Mohammad Arashi
openaire   +2 more sources

Combination of the modified Kibria–Lukman and the principal component regression estimators

Communications in Statistics - Simulation and Computation, 2023
Dan Huang, Jiewu Huang, Dewei Bai
openaire   +1 more source