Results 61 to 70 of about 133 (94)

A new estimator for the multicollinear Poisson regression model: simulation and application. [PDF]

Sci Rep, 2021
Lukman AF, Adewuyi E, Månsson K, Kibria BMG.   +3 more
europepmc   +1 more source

A new class of efficient and debiased two-step shrinkage estimators: method and application. [PDF]

J Appl Stat, 2022
Qasim M, Månsson K, Sjölander P, Kibria BMG.   +3 more
europepmc   +1 more source

A new class of Poisson Ridge-type estimator. [PDF]

Sci Rep, 2023
Ertan E, Akay KU.
europepmc   +1 more source

Modified ridge-type for the Poisson regression model: simulation and application. [PDF]

J Appl Stat, 2022
Lukman AF, Aladeitan B, Ayinde K, Abonazel MR.   +3 more
europepmc   +1 more source

Almost unbiased modified ridge-type estimator: An application to tourism sector data in Egypt. [PDF]

Heliyon, 2022
Omara TM.
europepmc   +1 more source

Some Ridge Biasing Parameter for Linear Regression Model and Their Performances on Kibria-Lukman Estimator

Multicollinearity, arising from the violation of the independence assumption among explanatory variables in a linear regression model, poses a significant challenge to parameter estimation. It inflates the variances of the Ordinary Least Squares (OLS) estimates, leading to unstable coefficient estimates and unreliable inference.
openaire   +2 more sources

Predictive modelling of COVID-19 confirmed cases in Nigeria. [PDF]

Infect Dis Model, 2020
Ogundokun RO, Lukman AF, Kibria GBM, Awotunde JB, Aladeitan BB.   +4 more
europepmc   +1 more source

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

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

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.
AKAY, Kadri Ulaş, ERTAN, Esra, ERKOÇ, Ali   +2 more
openaire   +3 more sources

Kibria‐Lukmantype estimator for gamma regression model

Concurrency and Computation: Practice and Experience, 2022
SummaryThe 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. However, the method performance drops when linear dependency exists among the predictors (multicollinearity). In this article, we develop a
Gladys Amos Shewa, Fidelis Ifeanyi Ugwuowo   +1 more
openaire   +1 more source