Results 111 to 120 of about 551 (134)

A New biased estimator and variations based on the Kibria Lukman Estimator

Istanbul Journal of Mathematics, 2023
Summary: One of the problems encountered in linear regression models is called multicollinearity problem which is an approximately linear relationship between the explanatory variables. This problem causes the estimated parameter values to be highly sensitive to small changes in the data.
K. Akay, Esra Ertan, Ali Erkoç
semanticscholar   +4 more sources

Jackknife Kibria-Lukman estimator for the beta regression model

Communications in Statistics - Theory and Methods, 2023
The beta regression model is a flexible model, which widely used when the dependent variable is in ratios and percentages in the range of (0.1). The coefficients of the beta regression model are estimated using the maximum likelihood method.
Tuba Koç, E. Dünder
semanticscholar   +2 more sources

Kibria–Lukman estimator for the zero inflated negative binomial regression model: theory, simulation and applications

Communications in Statistics - Simulation and Computation, 2023
The zero inflated negative binomial model is an appropriate choice to model count response variables with excessive zeros and over-dispersion simultaneously.
M. Akram, Muhammad Amin, Nimrah Afzal, B. M. G. Kibria   +3 more
semanticscholar   +2 more sources

Kibria‐Lukman type estimator for gamma regression model

Concurrency and Computation: Practice and Experience, 2022
The gamma regression model explores the relationship between a skewed response variable and one or more independent variables. The method of maximum likelihood is popularly adopted to model the relationship.
G. A. Shewa, F. Ugwuowo
semanticscholar   +2 more sources

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

Communications in Statistics - Simulation and Computation, 2023
Statistical inference with the ordinary least squares (OLS) estimator is frequently influenced when there is a multicollinearity in the linear regression model.
Dan Huang, Jiewu Huang, Dewei Bai
semanticscholar   +2 more sources

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 F Lukman
exaly   +3 more sources

Improved Kibria-Lukman Type Estimator:Application and Simulation

2023 International Conference on Science, Engineering and Business for Sustainable Development Goals (SEB-SDG), 2023
The method of ordinary least square (OLS) in the linear regression model is widely used in different fields for quite some time, and it is grossly affected by multicollinearity.
Benedicta Aladeitan, Kayode Ayinde, Adewale F Lukman   +2 more
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.
Ohud A. Alqasem, A.T. Hammad, M. M. El-Raouf, Ahmed M. Gemeay   +3 more
semanticscholar   +2 more sources

Selection of A New Biasing Parameter for the Jackknife Kibria-Lukman Estimator for the Negative Binomial Regression Model

NIPES Journal of Science and Technology Research
The negative binomial regression model (NBRM) is a generalized linear model that relaxes the restrictive assumption of the Poisson regression model when the variance is equal to the mean. The estimation of the parameters of the NBRM is obtained using the
Henrietta Ebele Oranye, K. C. Arum, F. Ugwuowo, S. Bashiru, T. E. Ugah, T. M. Adegoke, C. Anioke, Temitope Olufumilayo Alakija, Johnson Nnamdi Ezeora, N. G. Okoacha   +9 more
semanticscholar   +2 more sources

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).
Ayanlowo E.A, Oladapo D.I, Odeyemi A.S, Obadina, G.O   +3 more
semanticscholar   +2 more sources